Chapter 4: Hue
Table of Contents
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Background
The dissertation from which this chapter is excerpted explores ocularities and imaginations of British literature, from about 1880 to about 1940.
In Chapter 1, I introduce key terms in the theoretical framework I’m using, and the sense in which I use them, which is narrower than usual:
- imagination
- a cognitive process that translates text to mental images
- image
- a group of words that causes or prompts imagination, and therefore mental images
I argue that description may be understood through imagination, and that imagination may be understood by way of the eye.
Chapter 2 examines literary theory and science contemporary to this period, to begin to build the case for how the eye is an apt metaphor for understanding this literature.
Chapter 3 deals with methodology, and answers questions such as:
- Why computational analysis?
- How does computational/quantiative analysis fit in with other critical traditions?
- Isn’t this all an oversimplification? (It isn’t.)
Chapters 4-6 each deal with an aspect of vision:
- Chapter 4 treats hue, photopic vision, (retinal cones)
- Chapter 5 treats shape, scotopic vision, (retinal rods)
- Ch. 6. treats space, proprioception (I might end up cutting this one)
This is Chapter 4.
Introduction
Modernity is colorful. And British literature of the modern, or modernist period, is brighter, more colorful, and more visual than ever before. The fiction and poetry published in Britain in the early 20th Century contains more color words, more colorful objects, and more descriptions of color phenomena than it ever did, and measurably so. Color and visual phenomena become not only modes of description, ancillary to plot and narrative arc, but themselves become themes, subject matter, and organizing principles.
The quantitative analysis I describe below finds a steady increase in the proportions of color words, color expressions, and colorful objects, in British literature at the turn of the century. Fig. 1 illustrates this literary-historical increase in colorfulness, showing these proportions over time, for several hundred novels and collections of poetry, according to their publication dates. Each point in this figure represents a book, and the upward-sloping line represents their linear regression. This trend is exaggerated once we take into account generational differences in writing styles, and view these proportions by the authors’ dates of birth, as in fig. 2.
How and why does this visual turn take place? What precisely happens at the vertical spike, seen in fig. 2, around 1910? This chapter explains these chromatic aspects of the visual turn in modern British literature: their causes, effects, modalities.
On or About December, 1910: a King, an Art Exhibition, and a Comet
In her often-cited essay from 1924, “Mr Bennett and Mrs Brown,” Virginia Woolf famously asserts that December, 1910, was a turning-point for human character:
“My first assertion is … that everyone in this room is a judge of character. Indeed it would be impossible to live for a year without disaster unless one practised character-reading and had some skill in the art. … And now I will hazard a second assertion … that in or about December, 1910, human character changed.” (V. Woolf, Collected Essays by Virginia Woolf. Vol. 1 320)
The phrase “on or about December, 1910” appears over 1,192 times in publications since 1924,Mostly since 1980. I derive this total from raw data from this 5-grams archive, via Google Books.
and is the title of a monograph on the early Bloomsbury group (Stansky). In it, Peter Stansky cites a number of epoch-making events that straddle 1910. Among them are: the death of King Edward VII, signaling the end of the Edwardian era; Roger Fry’s London exhibition, “Manet and the Post-Impressionists”; and the passing, in early 1910, of Halley’s Comet. Each of these is worth addressing, not just as significant events of the new decade, but as events which contributed to the change in the literary ocularities that we see manifested in the figures above.
First, let us consider the end of the Edwardian era. The death of the “rich and vulgar” Edward VII, in May 1910, along with the coronation of George V in 1911, while less culturally significant than other events, nonetheless provided for many Brits a useful shorthand for circumscribing an era of extravagance (Stansky 1).Paul Thompson’s study of the Edwardians finds that “the top 1 per cent of Edwardians … owned 69 percent of the national capital,” a wealth, and an inequality, which “was the highest in modern British history and probably then the highest in the western world” (Thompson 2). We will return to this ostentation later, when dealing with material conditions of commercial pigmentation.
Woolf’s essay begins by dividing early 20th Century writers along these lines: “Mr. Wells, Mr. Bennett, and Mr. Galsworthy I will call the Edwardians; Mr. Forster, Mr. Lawrence, Mr. Strachey, Mr. Joyce, and Mr. Eliot I will call the Georgians” (V. Woolf, Collected Essays by Virginia Woolf. Vol. 1 320). Woolf’s list of British writers is very nearly in chronological order, by date of birth. The most senior of the “Edwardians,” H. G. Wells, was born in 1866, quickly followed by Arnold Bennett and John Galsworthy in 1867, while the “Georgian” E. M. Forster was born a decade later in 1879, followed by Lytton Strachey in 1880, James Joyce in 1882 (as with Woolf herself), D. H. Lawrence in 1885, and T. S. Eliot in 1888.
This generational difference—the motivating factor behind the analysis shown in fig. 2—Woolf sees as manifest in an internal cohesion of the writers’ works. While among the Georgians, or, in a proto-modernist work like Tristram Shandy, Woolf argues, “everything was inside the book, nothing outside,” the Edwardians, in contrast, “were never interested in character in itself, or in the book in itself. They were interested in something outside. Their books, then, were incomplete as books, and required that the reader should finish them, active and practically, for himself” (@ Collected Essays by Virginia Woolf. Vol. 1 327).
Woolf’s concern for “character in itself” and “the book in itself” hearkens back to the autoteleological aphorism of Aestheticism, “l’art pour l’art,” or “art for art’s sake.” (Aestheticism, the artistic and literary movement of the 1890s, was born of the late Victorians, a generation for whom modernists / “Georgians” felt affinity.) In fact, we might well call Woolf’s essay “Mrs. Brown for Mrs. Brown’s Own Sake.” But what is more important is the mode in which this autotelos is enacted: sight. A sight is useful, or useless, unto itself: it is the progenitor of action, and precedes it.
In comparing Wells, Bennett, and Galsworthy, by imagining the way each novelist would treat Mrs. Brown, Woolf’s train car neighbor, Woolf uses visual and ocular metaphors to contrast their writing styles: Wells would “project a vision on the window-pane” of a utopian world without Mrs. Brown. “And what would Mr. Galsworthy see?” Woolf asks (@ Collected Essays by Virginia Woolf. Vol. 1 327). (Crucially, this is not “what would Mr. Galsworthy write,” but what would he see.) He would see a symptom of a failing society, she replies. Bennett, however, “would keep his eyes in the carriage” (@ Collected Essays by Virginia Woolf. Vol. 1 328). Later, in describing Strachey’s “against the grain” biography Queen Victoria, Woolf argues that “Mr. Strachey has had to open our eyes before he has made us see” (@ Collected Essays by Virginia Woolf. Vol. 1 335).
This “make us see” is the very same which Conrad identifies as his goal, in the preface of The Nigger of the Narcissus, which I quote in Chapter 2. It is the goal of the novelist, according to Conrad, and to Woolf. In fact, Woolf acknowledges Conrad, in this essay, as one of the only exemplary writers available to the 1910 generation, most likely for his vision—that is, for his prose treatment of visual phenomena.
The hypothesis of active vision, in studies of visual perception, is one which
As Woolf sees it, the Georgians distinguish themselves through their sight—their eyes are “in the carriage” with Mrs. Brown, rather than lost in utopias, or concerned with grand societal problems. They see Mrs. Brown in detail, but they are not carried away with detail itself, and do not attempt to paint a complete picture. It is no coincidence that “Mrs. Brown”—Woolf’s invented name for this real person—is also the name of a color. Woolf’s description of her lists things that are likely brown, without needing to use the word: she is “threadbare,” and wears “clean little boots” (@ Collected Essays by Virginia Woolf. Vol. 1 323). Her appearance suggested to Woolf “extreme poverty” without needing “rags or dirt.” Her only line of dialogue is, “can you tell me if an oak tree dies when the leaves have been eaten for two years in succession by caterpillars?”—a scene again full of brown things, like dead trees. In contrast, the villain of this story, Mr. Smith, is considerably richer, sporting “blue serge.”
Woolf’s Edwardian/Georgian distinction is one of convenience, as she herself is quick to admit, and had Edward VII died ten years later, it likely would not have affected the novels of the 1910s.
An even more substantive event of the year is Roger Fry’s exhibit in 1910. The art critic and painter Roger Fry, a late entry into Woolf’s Bloomsbury group of friends, organized, at the Grafton Galleries, one of the most influential British art exhibitions of the early 20th century: “Manet and the Post-Impressionists,” the exhibition which coined the term “post-impressionist.” Like its predecessor, impressionism, post-impressionism, places greater importance on bright color and form than in representation, drama, or chiaroscuro. Its apotheosis, in terms of this emphasis, is fauvism, the trend, circa 1905–1908, for reducing paintings to large swaths of extremely bright colors.
To speak precisely, post-impressionist paintings are, according to my image analysis, 37.4% brighter than European paintings of the early 19th Century.I arrive at this figure with this analysis: by querying Wikidata for paintings labeled with the movement “post-impressionism,” by querying for paintings created between 1800–1850, by downloading one hundred images from each category, and by computing K-means centroids for their luminosity in HSL color space (hue, saturation, luminosity).
The controversial exhibition shows this new brightness, featuring the titular Edouard Manet, along with Paul Cézanne, Paul Gauguin, Maurice Denis, Vincent van Gogh, and other painters known for their use of bright color.
A cartoon in the November 1910 issue of The Bystander illustrates the controversy (The Bystander 375). A segment of the cartoon labeled “you arrive thus” depicts two gentlemen arriving at the exhibition: one half-asleep, and the other, alert. The following segment, labeled “and depart thus” depicts the sleepy man suddenly alert, and the alert man with buckled knees, wiping the sweat from his brow. Another corner of the cartoon depicts an appreciative look from “an American art student who liked the colour harshness.” Finally, a tableau at the bottom of the page shows four men and three women, in genteel hats and costume, doubled over in laughter. The caption is “From the Pictures’ Point of View.”
Even Roger Fry’s own comment on his exhibition had, in his biographer Virginia Woolf’s phrase, “an apologetic air” (V. Woolf, Roger Fry 153). “There is no denying,” Woolf quotes Fry as saying, “that the work of the Post-Impressionists is sufficiently disconcerting. It may even appear ridiculous to those who do not recall the fact that a good rocking-horse has often more of the true horse about it than an instantaneous photograph of a Derby winner.”
Thes reactions were not an exaggeration. As Woolf put it:
“it is difficult in 1939, when a great hospital is benefiting from a centenary exhibition of Cézanne’s works, and the gallery is daily crowded with devout and submissive worshippers, to realize what violent emotions those pictures excited less than thirty years ago. … The public in 1910 was thrown into paroxysms of rage and laughter. … they were infuriated. The pictures were a joke, and a joke at their expense. … The pictures were outrageous, anarchistic and childish. They were an insult to the British public and the man who was responsible for the insult was either a fool, an impostor or a knave” (V. Woolf, Roger Fry 153–54).
“Anarchistic and childish.” These reactions, while extreme, bear some examination. First, “childish” is telling, given the well-documented preference among children for bright colors (@ See, e.g., Boyatzis and Varghese). In fact, Woolf relates the experience of fellow Bloomsbury member Desmond MacCarthy, to whom “Parents sent … childish scribbles which they asserted were far superior to the works of Cézanne.” (V. Woolf, Roger Fry 154). Second, the reaction that the paintings were “anarchistic” speaks to a related thread of so-called primitive art that was incipient in modernism. These two observations are strikingly similar to these two points which art critic C.J. Holmes makes of the exhibition:
“The tradition of Post-Impressionism, then … is the expression of a personal vision: 1. Through the methods, first applied to oil-paintings by the Impressionists, which aim at the greatest possible vibrancy and luminosity of colour, obtained by the juxtaposition of pure bright pigment in small separate touches. … 2. Through rigid simplification on the lines of the Orientals and of Daumier, in which the means of expression are reduced to line and colour …” (Holmes 19)
The “pure bright pigment in small separate touches” is a technique borrowed from impressionism, and its sister style, pointillism: it is one which leverages the ocular / neurologica interactions of color to produce new colors. The “lines of the Orientals” speaks to the influence of far Eastern art—in particular Japanese woodblock prints—that would become important for modernism, and which would come to its climax in the 1920s, with such works as “The Waste Land” in 1922, the first British performance of Stravinsky’s ballet “The Rite of Spring” in 1921. As we will see later, rich visual descriptions are often correlated with geographic or imaginitive distance, and so appear often in travel narratives, period pieces, science fiction, and other genres of distance.
But the point I am making here is not only one of the art-historical milieu in which modernist literature is steeped, but that this way of thinking about color perception is crucial to understanding the use of color words, in descriptive prose or poetry, since color words, like “pure bright pigment,” are single brushstrokes that belong to their greater pictures—that is, textual context.
The influence of this exhibit, for the writers, thinkers, and artists living in London, is difficult to overestimate.
A less influential, yet still significant, event of the 1910s was the arrival, in the early part of the year, of Halley’s Comet. A literal once-in-a-lifetime event, happening only every 75 years, the comet had long appeared as an omen at various turning-points in British and world history. It appeared in the year of the Norman conquest of England in 1066, and was depicted in the Bayeux Tapestry, which is its first known representation. In the late 17th Century, British astronomer Edmond Halley, in dialogue with Isaac Newton, noted its periodicity. Its arrival in 1910 was special, however, since it would come closer to the earth than ever before. Even more concerning was that recent developments in spectrometry found, in 1909, that its tail contained the poisonous gas cyanogen (Stoyan 147). This caused mass hysteria in Europe, where gas masks, bottles of oxygen, and “comet pills” were sold in vast quantities.
All of this was fueled by comet-related catastrophe in science fiction: Edgar Allen Poe’s story “The Conversation of Eiros and Charmion,” Jules Verne’s novel Hector Servadac (or “Off on a Comet,” in English editions), Camille Flammarion’s La Fin du Monde, and H.G. Wells’s 1906 In the Days of the Comet, the only one to speculate about positive effects of extraterrestrial gases. There, the comet’s gases has the effect of opening the minds of the world’s denizens, allowing them to engage in free love and polyamory.
Although British intellectuals, on the whole, did not buy in to this comet panic, or liberation, its effects were still felt. John Maynard Keynes, an economist in the Bloomsbury Group, wrote, “the comet’s tail last night produced a very odd state of affairs in the air here. It was oppressive & electric & rather exciting” (Stansky 120).
George Dangerfield’s influential 1935 history, The Strange Death of Liberal England, begins with the 1910 appearance of Halley’s Comet, and the death of King Edward VII. “Upon the chill and vacant twilight blazed Halley’s Comet – which, visiting the European heavens but once in a century, had arrived with appalling promptness to blaze forth the death of a king” (Dangerfield 19). He writes, in his characteristically dramatic style, that 1910 is “a landmark in English history, which stands out against a peculiar background of flame. For it was in 1910 that fires long smouldering in the English spirit suddenly flared up, so that by the end of 1913 Liberal England was reduced to ashes” (15-16).
These three events are reflective of, if not emblematic of, the changes in human character which Woolf identifies as happening in 1910: they signal the strange death of the old beige order, and the coming of a new, brightly colored era.
But history is not a collection of events, and events misrepresent the slow change that we see enacted in the cultural trends above.
To better understand the wave of color in 1910, we need to go back a generation further, to the 1890s.
The Yellow Nineties: Decadence, Cosmetics, and Artificiality
Despite what Woolf might have us believe, human character, and with it a sense of colorfulness and brightness, didn’t change all of a sudden in 1910, but was a gradual change, with a large number of causes.
One of the most significant, and one to which Woolf alludes, is the cultural environment of the late Victorian period: the 1890s, or “yellow nineties” as it was often known. This period saw a trend towards the celebration of artificiality: synthetic pigments and bright colors, exemplified by artistic movements such as the aestheticists / Decadents, and the pre-Raphaelites. Yellow was its color in more than one respect. As Frances Winwar puts it:
The color had been a favorite one with Rosetti, Morris and Burne-Jones who had first discovered it in the richness of medieval panels, then nearer at hand in the sunflower of rural gardens. … There was something vivid and daring about the color, something of the times, like the golden bloom of the age on a century that was nearing its close—not to death but to greater achievement. Yellow and fin-de-siecle began to have connotations open to many meanings but all leading to a definable sense of modernity, challenge, emancipation. People were no longer afraid to live. … The public became yellow-conscious. (Winwar 239)
Yellow had become a symbol of modernity. The journal at the center of this trend was The Yellow Book, a short-lived illustrated quarterly journal of literature and art, published between 1894–1897. The bright yellow color of its cover recalled the look of racy French novels of the day, which were similarly bound (Wilde xx).
Holbrook Jackson describes its impact thus: “Nothing like The Yellow Book had been seen before. It was newness in excelsis: novelty naked and unashamed. People were puzzled and shocked and delighted, and yellow became the colour of the hour, the symbol of the time-spirit. It was associated with all that was bizarre and queer in art and life, with all that was outrageourly modern” (Jackson 54).
Its inaugural issue began with a sketch by the pre-Raphaelite painter Frederich Leighton, the Henry James story “The Death of the Lion” which parodies literary canonization, and an essay by Max Beerbohm titled “A Defense of Cosmetics.” It begins, “it is useless to protest. Artifice must queen it once more in the town” (Beardsley 65). The “artifice” he exhalts is that of cosmetics, or the cosmetic, more generally. The epoch, as he sees it, is changing, and this has a distinctly visual effect: “For behold! THe Victorian era comes to its end and the day of sancta simplicitas is quite ended. The old signs are here and the portents to warn the seer of life that we are ripe for a new epoch of artifice. Are not the men rattling the dice-box and ladies dipping their fingers in the rouge-pots?” (Beardsley 65). Rouge, of course, is the cosmetic product, usually worn on the lips and face, and which takes its name from the French word for red. It is usually reddish in color, but not always. It not would have been fashionable for Victorian women, apart from actresses, to use rouge, since, as one historian of cosmetics put it: “women of the [Victorian era] had to disguise any attempts at self-improvement. The prudery of contemporary moral standards was totally prohibitive as far as female vanity was concerned …” (Gunn 137). But this was starting to change by the 1890s.
Culture of yellow
Development of lithography
Old
Among the most striking ocularities of literary description, and more broadly of any narrative, are pauses to convey the hue of that what is before the describer’s imagination. Pauses because description is a dilation of extradiegetic time. Before in that the described, if it has hue, is in focus, in the center of the describer’s field of vision. To describe is to settle one’s gaze in a field, and at the same time, to move it linearly and programmatically across that field. It is always selective, and it always refracts. Description of hue is among the most concentrated subroutines of that process, since the creation of textual color from a mental image involves digitization: approximative translation from an analog, linear system (a spectrum) to a digital, discrete system (a word). This process is so deeply involved with our language/thought apparatus, and so charged with epistemological problems, that it is the perennial subject of linguistics, neuroscience, and psychology. But rather than explore the phenomenon of textual color theoretically, I will take the opposite approach, and model the imaginative process in reverse, and in aggregate. Modeling is a statistical mimesis. Given observable qualities of a subject—in our case, a text, or a corpus of texts—a model imitates those qualities, in order to study the subject’s behavior under unobserved circumstances. In modeling a hurricane, a meterologist not only becomes capable of predicting that hurricane’s path, albeit with some margin of error, but, by studying the model’s error, learns further nuances of the system he or she is studying. To model literature is not to discover how Virginia Woolf would have written about the 2020 coronavirus epidemic—although that sort of prediction is possible—it’s to understand the small swirls of rainwater that compose the greater phenomenon we know as the hurricane. I am motivated here, both by large questions about literary history, and smaller, more specialized ones about individual texts. Big ones include: Does literary writing get more colorful with time? (It does.) What are the dominant colors of this period’s literature? (White and black.) and, Which genres are the most colorful? (Love stories.) But smaller questions have to do with the way color words operate syntactically, how they operate within description, and how their semiologies warp the reader’s imagination. When Woolf describes a set of curtains as “mustard-coloured,” or when Joyce describes a man’s eyes as “nocoloured,” how and why do those color choices do more work than their superficial significations? How, precisely, are color words used differently in poetry and fiction? These questions are inseparable from the way they are modeled. In many cases, modeling them is what generates the questions. In others, the model is, at least partially, the answer to these questions. It is with that in mind that I invite you to join me in my process of creating this model of imagination—an imagination machine—where each decision in the algorithmic design, however mathematical, probes at the workings of color in text. ** A Critique of the Narration / Description Dichotomy Before we begin our experiment, it is necessary to discuss the textual structure in which representations of hue are typically found: literary description. Although textual color has its own behaviors and properties, the conditions of description shape how color operates in text. By description here I am discussing a very particular writerly process which linearizes and descretizes visual information: that which transforms imaginable material into words, and arranges those words into lines. Exactly what may be identified as description, and what its role and import in literature may be, has been a matter of some debate. One of the more hotly contested works is an essay by Georg Lukács, titled “Narrate or Describe?” the central dichotomy of which is apparent from the title (Lukács). Lukács contrasts writers such as Flaubert and Zola, whom he calls “descriptive,” with writers like Tolstoy whom he claims use a more “narrative,” action-oriented style. The descriptive style Lukács is quick to dismiss. Of descriptive details in Flaubert’s Madame Bovary, he writes: #+BEGINQUOTE to the reader they seem undifferentiated, additional elements of the environment Flaubert is describing. They become dabs of colour in a painting which rises above a lifeless level only insofar as it is elevated to an ironic symbol of philistinism. The painting assumes an importance which does not arise out of the subjective importance of the events, to which it is scarcely related, but from the artifice in the formal stylization." (115) #+ENDQUOTE He later asserts that description “lacks humanity,” in that “its transformation of men into still lives is only the artistic manifestation of its inhumanity.” (140). I join the many later critics who have written about Lukács’s essay in disagreeing with him, but since they critics have done this so thoroughly, I won’t bother to do so here (Love; Marcus et al.). My refutation is much more radical: I question the distinction between narrative and description at the root of Lukács’s argument. There do exist some aspects of fiction that have no descriptive function, of course—they may not be imagined, and thus they convey no images. But nearly everything else in fiction does describe. The opposite is nearly true, as well: there exist very few elements in a story that are purely descriptive, and serve no role in furthering the plot, fleshing out the characters, or providing a scene which is inextricable from, and indespensible to, plot and character. In other words, to narrate is to describe. Any text may be description if it contains a visual component (strong description) or may be imagined (weak description), and this includes the “epic artistry” Lukács sees in Tolstoy, and his “recounting of the vicissitudes of human beings” (111). Since, is not the visual experience one such vicissitude? This is about more than just a distinction between the stylistic pastoral and epic, though, where description recounts in minute detail because it has the bourgeois leisure of a shepherd, and narration practically presents the facts, with military precision. Description is not essentially static, even though it often is. The proof is simply that action can be, and is, imagined in the same way as a still-life. Furthermore, description’s linearity makes it a priori dynamic. To explain this further, I’ll use a well-known metaphor from physics: the color spectrum. Although there do exist areas on a color spectrum where we could identify certain colors—spots at which one could point, and nine out of ten English speakers would call it /red/–if these same people were asked to draw a line that definitively separates red from pink, or red from blue, there would be ten very different answers. The same is true of description and narration: they exist along a spectrum, and overlap with each other considerably. It is in that sense that we could say that the distinction isn’t real at all. This is a crucial context for understanding textual color. At first glance, and according to Lukács’s followers, color is probably the one element of fiction most superfluous to the story, and thus the work’s reason for being. But what I argue is that colors in text are not simply signifiers of their position on the visible spectrum, but are the material out of which the text is created.
Mechanics of Color and Description
Problems and First Considerations
The first of many color-related epistemological problems may be found among color metaphors like lemon-yellow. Lemons themselves are—paradoxically—not lemon-colored. But neither is lemon-yellow a Platonic ideal to which all lemons, or paintings of lemons, should aspire. Instead, the term exists somewhere between lemons, our memory of them, our visual experience of them, and what we read about them. This view is allied with Heather Love’s 2016 work, “Shimmering Description,” which characterizes literary description as oscillating, or shimmering, between its lexemic and communicated significance. Beginning with Love’s idea, I aim to find the dimensions in which this oscillation takes place.
Aloys Maerz and Morris Paul’s 1930 reference manual A Dictionary of Color, one of the more ambitious works of its kind, acknowledes this problem as one they hope to solve with their manual. They see this as a part of the “material” and “intellectual” confusions of color names:
the confused ideas on color nomenclature are found due to two factors, one material, the other intellectual. The first has been the ability of color makers, in the past, to produce color substances that were both brilliant and permanent … the second is the difference of opinion as to the exact color indicated by any name, and the lack of any authority by which an individual opinion can be upheld. … the name Lemon Yellow would seem sufficiently accurate as a descriptive term, yet the color of lemons varies slightly and the memory for exact color sensations, when the original is not at hand, is often faulty. (Maerz and Paul 1)
Readers of James Joyce’s Ulysses may recognize the color lemon-yellow here. Lemons and lemon-yellow are leitmotive that appear at intervals in the novel. First appearing in the Telemachus episode as the “Paris fad” for tea which Buck Mulligan rejects in favor of “Sandycove milk” (Stephen has just recently returned from Paris, and had aquired some of its habits), the color appears in “Proteus,” as Stephen muses about the effects of sunlight on the color of the houses: “Gold light on sea, on sand, on boulders. The sun is there, the slender trees, the lemon houses. ¶ Paris rawly waking, crude sunlight on her lemon streets” (Joyce, Ulysses 10, 35). Neither the Sandymount houses nor the Paris cobblestones are painted lemon-yellow, of course, or appear so at all other times of day, but they look this way under the reflection of the early morning light. Stephen, a poet, is more interested in the phenomenology of the visual experience than its lexicon—one which would describe the houses by the name of their paint, or the stones as gray. Lemon-yellow, then, is the site at which the Aristotelian conception of color–the stones are gray—meets Newtonian color phenomenology.
Leopold Bloom, too, the hero of Ulysses, imagines the skin of his naked body in the bath, as “lemonyellow,” not because he is jaundiced, or of olive-toned Mediterranean complexion, but because he imagines the light catching his body, “oiled by scented melting soap,” the lemon-scented and lemon-colored soap he’d just bought (Joyce, Ulysses 71). When Bloom later notices the scent of “citronlemon” in his handkerchief, he conflates the citron, an ancestor of the lemon and the French word for lemon, with Israel Citron, a real Dubliner about whom he had been thinking two paragraphs earlier (Gifford, Ulysses Annotated 74, 133). Don Gifford suggests that Bloom “associates the soap with the citron (Ethrog) central in the ritual of the Jewish Feast of Tabernacles (Sukkoth) (133). In the surreal dream of the Circe episode, this soap reifies,”diffusing light and perfume," and speaks in terms of light and reflections: “we’re a capital couple are Bloom and I. He brightens the earth. I polish the sky” (Joyce, Ulysses 340). For Bloom, colors like lemonyellow are a crucible where visual experience, other sensory experiences, and memory are melted together.
But not only are these textual perceptions problematic, but, as Maerz and Paul remind us, the color of lemons themselves varies. In fact, lemons themselves are green before they ripen, and green in certain varieties. In French, a language in which Stephen often daydreams, lemons and limes are citron and citron vert, (“green lemons”) most commonly, meaning that lemons can be both yellow and green, in that language’s taxonomy. However, the color lemon, in English and in French, invariably refers to a bright yellow, despite any variation in its actual color. This is a theoretical problem now, but will become a practical problem, in the section below, on modeling color categorization. Everyone knows that lemons are yellow, blood is red, and the sea is blue. But lemons are also green, blood is usually brownish, and the sea may appear purple, brown, or green. So description, then, whether literary description or otherwise, is both a representation and a social contract.
On the Impossibility of a Bluish Yellow
A second, more troubling, and more deeply epistemological problem Ludwig Wittgenstein articulates in his late work Remarks on Color. He asks, quite simply, whether it is possible to imagine a “bluish yellow”:
If you call green an intermediary colour between blue and yellow, then you must also be able to say, for example, what a slightly bluish yellow is, or an only somewhat yellowish blue. And to me these expressions don’t mean anything at all. But mightn’t they mean something to someone else? (Wittgenstein and Anscombe 20e)
Wittgenstein then asks whether a “reddish green” or other color combinations might be difficult to imagine, and why. He posits that the category of green is what prevents him from imagining “bluish yellow,” since, he says, “for me, green is one special way-station on the coloured path from blue to yellow…” (Wittgenstein and Anscombe 22e). This is an important question, with many implications. First, what colors are there which have greater primacy among speakers of English? And more generally: why do linguistic categories—color words and their weights in our language—transform our imaginative processes?
I say “our” here with some hesitation, since I suppose an affinity with others who might experience color terminology in the same way, but recognize that a painter, with years of experience mixing colors, might imagine these terms differently, as would, most likely, a speaker of a language very different from English. Still differently would a blind person imagine these colors.
This question of Wittgenstein’s is testable, to some degree, by examining patterns in literary data. To test this, I constructed a matrix of color expressions from the \(CM_X\) color map,Described in more detail below.
where one word ends in -ish. The resulting matrix is shown here in fig. 3.
| color | purplish | greenish | bluish | greyish | tealish | reddish | pinkish | lightish | brownish | darkish | purpley | yellowy | bluey | yellowish | purpleish | orangish | light | orangeish |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| red | purplish red | nan | nan | nan | nan | nan | pinkish red | lightish red | brownish red | darkish red | nan | nan | nan | nan | nan | orangish red | nan | nan |
| blue | purplish blue | greenish blue | nan | greyish blue | nan | nan | nan | lightish blue | nan | darkish blue | purpley blue | nan | nan | nan | purpleish blue | nan | light greenish blue | nan |
| brown | purplish brown | greenish brown | nan | greyish brown | nan | reddish brown | pinkish brown | nan | nan | nan | nan | yellowy brown | nan | yellowish brown | nan | orangish brown | nan | nan |
| pink | purplish pink | nan | nan | greyish pink | nan | reddish pink | nan | nan | brownish pink | darkish pink | purpley pink | nan | nan | nan | purpleish pink | nan | nan | nan |
| grey | purplish grey | greenish grey | bluish grey | nan | nan | reddish grey | pinkish grey | nan | brownish grey | nan | purpley grey | nan | bluey grey | nan | nan | nan | nan | nan |
| yellow | nan | greenish yellow | nan | nan | nan | nan | nan | nan | brownish yellow | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| teal | nan | greenish teal | nan | greyish teal | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| tan | nan | greenish tan | nan | nan | nan | nan | pinkish tan | nan | nan | nan | nan | nan | nan | yellowish tan | nan | nan | nan | nan |
| turquoise | nan | greenish turquoise | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| beige | nan | greenish beige | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| cyan | nan | greenish cyan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| purple | nan | nan | bluish purple | greyish purple | nan | reddish purple | pinkish purple | lightish purple | brownish purple | darkish purple | nan | nan | bluey purple | nan | nan | nan | nan | nan |
| green | nan | nan | bluish green | greyish green | tealish green | nan | nan | lightish green | brownish green | darkish green | nan | yellowy green | bluey green | yellowish green | nan | nan | light bluish green | nan |
| orange | nan | nan | nan | nan | nan | reddish orange | pinkish orange | nan | brownish orange | nan | nan | nan | nan | yellowish orange | nan | nan | nan | nan |
Not only are there no entries for bluish yellow or reddish green here, but a few other patterns are apparent. First, yellowish green is not mapped to the same color as greenish yellow, indicating that the order of the adjectives dictates precedence. Second, those colors that take -ish adjectives are common colors. However common a color like maroon might be, reddish maroon does not appear in this list, potentially because it’s not considered a basic color with the ability to be mixed. However, some colors which are common in marketing, like beige and teal, but which are less common in paint names, are present here.
Also note that orangeish and orangish, variant spellings of the word, have different average colors here, and orangish is used as a modifier half as much as orange is modified by an -ish. We might say, that orange can -ish, but it is not very -ishable. Greenish and brownish are much more versatile as modifiers than others: they are good -ishers. But green is much more easily /-ish/ed than other colors. So does green take a first place in our cognitive pantheon, despite being a secondary color?
Pink has many variations here, despite being simply a shade of red. We don’t see these same patterns with an analog of pink in other hues, like light blue or light green. This leads one to believe that pink’s monolexemic and monosyllabic advantage over analogues like light blue give it more cognitive-categorical weight.
There are many, many more puzzles to be found by exploring the semi-permeable membrane of the color/word divide. But these should be enough for us to remember before proceeding to the algorithmic design of this computational model, where we will encounter more of these problems.
Colors and their Object-Archetypes
Things and their colors define each other. In a metaphoric sense, things are colors: we use things to describe our visual experience. Alternatively, our color categories are shaped by the kinds of things we often see which are seemingly illuminated by these colors. The sky is blue is a statement of such obvious fact that the phrase has come to emblematize obvious facts themselves. The sky is blue, leaves are green, roses are red, and violets are blue. Or are they violet?
Put differently: are violets named such because they are violet in color, or is the color word violet the name for the color of the flower? Lexical data from the OED show that violet the color appears at least a hundred years after violet the name of the flower—in 1430 and in 1330, respectively. (“Violet, Adj.”). Similarly, we might ask, which came first, the color orange or the orange fruit itself? And there, too, the name of the fruit, taken from the name of its tree, is ultimately descended from Sanskrit, and is older than English itself, but the color sense only appears in the early 16th century. (“Orange, Adj.”). So we might deduce that the polysemy between these colors and their associated objects is caused by the phenomenon of naming colors after common objects.
But in other cases, bigrams encode a magnetism between the object and its appearance. In designing the experiment below, I wanted to know: what objects are most often described as blue, red, and so on. I wanted to quantify the gravitational pull of colors with their associated objects. To investigate this, I use data from the Google Books Ngram Viewer project, hereafter \(C_{NG}\) (Google Books Ngram Viewer Exports). Google Books provides n-gram (sequences of words of length \(n\)) data for the books in its vast collection, and the most recent version, 20200217, provides n-grams tagged according to their parts of speech. I use the data subset English Fiction, which, although not strictly relegated to the time and place this I am studying here, is still useful to determine broad patterns.I describe this corpus in greater detail in the appendix below.
There, I query for patterns ADJ NOUN, where ADJ is a color word, tagged as an adjective, and NOUN is any noun which follows. The list of color words I derive from Berlin and Kay, but augment with several auxiliary color words, for comparison (Berlin and Kay).
Fig. 4 shows word cloud visualizations for each color word and its most commonly collocated nouns.Word clouds, or tag clouds, are a relatively recently popularized technique of textual data visualization, which depicts the frequency of words through typeface sizes. See (Viégas and Wattenberg) for a history of the visualization that traces it to Soviet Constructivism.
Surprisingly, the most frequent collocations are not the most cliché: green grass and green leaves are of course present, but are not as frequent as green eyes. Blue sky is present, and red blood, but are subordinate to hair and eyes.
Some overall trends are apparent in these words, which may be illuminated by categorizing them. Using WordNet, the relational lexical database, I am able to identify hypernyms for most of these words, and group the words according to these hypernyms (Miller). The hypernym treemap for red, for instance, shows that a good proportion of the words are body parts (they have the hypernym synset bodypart.n.01), or “coverings” such as hair (covering.n.01). A crucial similarity between these bodily descriptors, from red hair, the most frequent collocation, to red eyes, red lips, and red face, is that they describe exceptions, or aberrations, from their usual states. Red hair (actually orange, as I will argue below) is among the least common natural hair colors. Red eyes describe diseased or depigmentized eyes, of humans or other animals. And red lips are lips that are unusually red: whether blood-filled through vigor, or excitement, or through the use of cosmetics.
Artificial objects comprise a second, equally large, hypernym, artifact.n.01. These are, with a few exceptions like brick, items which have been dyed red: silk, velvet, dress, shirt, tape, lipstick, carpet. If silk, or a dress, were always red, we might not need to describe it as such—it would be obvious. But since these are all items which are typically dyed, at least in modernity, they need to be described according to their dye. As with the read body parts above, the dye here is the difference, or the abnormality, which necessitates the color description.
This leads me to a theory of color description: that color descriptions are color exceptions.
Color descriptions are color exceptions
We are blind to things that aren’t important to us, or exceptional in some way. It’s not that the light, at certain color frequencies, doesn’t reach our eyes, but it isn’t processed by our brains in the same way. Thus, what we describe, using color words and color expressions, is what we have noticed, or what we want to be noticed: something different, striking, or unusual. This is why red hair is a more frequent collocation than black hair or brown hair, despite the rarity of the gene that causes red hair.
At this point, you may fairly imagine a number of counterexamples, not the least of which are those clichés I’ve just outlined: the blue sky, or the even wine-dark sea. I would argue that, in most cases, these are a special kind of exception: one of magnitude, rather than category. In other words, when a writer describes leaves as green, it is less often a pure cliché than a calculated underscoring of the visuality of the leaves: that they are unusually green, noticeably green, or a particular subcategory of green.
Here is a passage from Jacob’s Room, Virginia Woolf’s novel, which, incidentally, I will later show is among the most colorful in this period of British Literature:
The tree had fallen, though it was a windless night, and the lantern, stood upon the ground, had lit up the still green leaves and the dead beech leaves. It was a dry place.(Virginia Woolf, Jacob’s Room 21)
Here, green leaves describes an exception to the rule in which living leaves are green, and dead leaves are brown. But this theory remains manifest upon closer examination of some of the bigrams of fig. 4 as they appear in \(C_{PG}\).A full concordance is available here.
There, the phrase green leaves is rarely unaccompanied by an additional modifier. Leaves are light green, emerald-green, or sea-green: specificities that take color knowledge, via visual abberation, into the realm of color description.
In James Joyce’s 1914 short story collection Dubliners, the boy protagonist of “An Encounter” describes trees along the canal bridge:
All the branches of the tall trees which lined the mall were gay with little light green leaves and the sunlight slanted through them on to the water.(Joyce, Dubliners)
And in John Galsworthy’s The Dark Flower, a colorful novel, as it narrates the life of a visual artist, we read of “a blue sky thinly veiled from them by the crinkled brown-green leaves” (Galsworthy 66). Sometimes the word-order is different, even though the same syntactic dependence remains: in May Sinclair’s Mary Oliver, a Life, one of the most colorful novels as measured in the analysis below, we see “green leaves” which “had the cold glitter of wet, pointed metal” [TODO: cite]. Sinclair is not content with a description which presents leaves as green, but presents them with alien qualities. These leaves appear so unusually, she implies, that the play of light on their surfaces appears as if their material were entirely different.
It may seem as if this argument—that color descriptions are visual anomalies—deflates upon scrutiny. After all, it’s obvious that we don’t need to say the obvious. But this theory will be born out materially as we begin to reverse-engineer textual imagination, which we will now see.
Imagining Words: Mapping Words to Colors
To model imagination, we start by working backwards, by first creating an engine to generate a color from a word or phrase. Given a word or group of words, we infer a color code: the exact proportions of red, green, and blue needed to recreate it computationally. We can either conceive of this process as modeling the writer’s imagination in reverse, or modeling a reader’s imagination. Of course, the question then becomes: which reader’s imagination do we attempt to model? One approach would be to model as many imaginations as we can, by averaging several mappings, from several different sources. But these sources vary greatly. So factors we would look for in a color/word mapping would include:
- Consensus. Color names should not be too subjective, since we want language that can be evocative with some degree of reliability. To this end, word/color pairs that appear in more than one map should be weighted higher than those that only appear in one.
- Synchronicity. The color names should not be anachronistic to the texts we are trying to understand. So a color like cyberspace blue is not very irrelevant to an understanding of a Virginia Woolf novel. However there is a sense in which it does: the imagination of a contemporary reader applies to his or her understanding of a literary work.
- Syntopicity. Army green and navy blue refer to the uniforms of their respective countries. However, the proliferation of these colors between militaries makes this difference small.
- Objectivity. We need to mitigate the influence of marketing on color naming. Paint manufacturers and similar organizations have a way of describing colors that are meant to sell paint: they skew towards pleasant color names. Yet not all colors are pleasant ones.However, colors on the whole do skew towards pleasant ones. Since colors are only perceptible in one’s central vision, and not peripheral vision, color perception betrays attention.
- Size. It would be best not to exclude colors simply because they don’t appear in a pack of Crayola crayons. Yet the more colors one includes, the more chances there are of metaphors that are more subjective, and farther afield.
A related issue is the algorithm by which we decide to collapse color word orthographies:
- Fuzziness. Blue-green and blue green should be categorized together as the same color. Yet blue! Green, that is, at the end of one sentence and the beginning of another, should not be categorized together.
- Absinthe green should match absinthe as well as absinthe green.
- Green and greenness should be in the same family, but not necessarily as synonyms, since greenness connotes something more abstract.
With these principles in mind, I chose several books, and several databases containing color/word mappings, and combined them into one master map, although I will occasionally use individual databases when appropriate.
Heuristic Maps
The breadth of the text/color translation problem is suggested even in at a glance at its bibliography: dictionaries of color, or manuals of color nomenclature, were essential reference books for centuries, not only among visual artists, designers, and others that work with pigment, but among botanists, ornithologists, and anyone else in need of a standardized way to describe visual phenomena. These manuals invariably contained color plates—some hand-painted, even—intended to be concrete mappings between color words and their associated hues. I chose just a few of these, based on their proximity to the period, the availability of their electronic editions, and/or the number of colors they contained.
\(CM_R\), Ridgway
Some of the most ambitious attempts at mapping colors to their names, or naming colors, came from the natural sciences. American ornithologist Robert Ridgway (1850-1929), for example, authored two influential works of color naming systems: A Nomenclature of Colors for Naturalists in 1886, and Color Standards and Color Nomenclature in 1912. In the preface of the earlier work, Ridgway names as his problem that “the author has in collection considerably over three hundred water-colors, each bearing a different name” (Ridgway X). This volume contains over a thousand colors, but the color names are less metaphorical (lemon-yellow), and more descriptive (bright yellow).
\(CM_S\), Saccardo
A continental work of the same decade is the ambitious and polyglot volume from Italian botanist Pier Andrea Saccardo bearing the formidable Latin title, Chromotaxia Seu Nomenclator Colorum, Polyglottus, Additis Specimibus Coloratis ad Usam Botanicorum et Zoologorum (1894). Although containing only fifty colors, it features an index of several hundred “synonyms” for these colors in Latin, Italian, French, English, and German. While some of these are recognizable to modern readers, others seem strangely specific, such as Murinus (mousey) or Fuligineus (“sooty”). Saccardo provides two supplementary colors: achrous, or colorless, glassy; and sordidus, or “sordid,” “dirty,” which he describes as a modifier rather than a color. “non est color definitus sed indicat inquinamentum aliorum colorum. Exempla: sordide albus, luride ruber” (Saccardo 16).
\(CM_M\), Maerz and Paul
Maerz and Paul’s 1930 A Dictionary of Color provides the largest number of colors, and was itself meticulously compiled from a number of prior manuals. This volume contributes over three thousand colors.
\(CM_P\) Pantone
The Pantone set, one of the most common among designers and artists today, contains over two thousand colors, but they have names which are much more mercantile than others. Thus, these are biased towards food-related words, flower-related words, or anything else that would seem like a pleasant marketing term.
\(CM_X\), XKCD
The antidote to the Pantone set is one from Russell Monroe, an American author, former NASA engineer, and cartoonist best known for his webcomic XKCD. Monroe surveyed his wide readership, asking them to name colors they were shown at random on his website. He also took demographic data from them, logged their locations via their computers’ addresses, and asked them whether they were colorblind, or used a cathode ray tube monitor. The survey results, which represent the five million color mappings from 220,500 users, show a consensus for many color names, as shown in table 1.I’ve represented these hex values in RGB space using a script which displays them in the browser, overlaid on the hex value itself, to account for color differences in monitors, pages, and other media.
| Color Name | RGB Hex Value |
|---|---|
| purply blue | #661aee |
| silver | #c5c9c7 |
| sickly green | #94b21c |
| melon | #ff7855 |
| mocha | #9d7651 |
| coffee | #a6814c |
| canary yellow | #fffe40 |
| purpleish | #98568d |
| bluey purple | #6241c7 |
This mapping presents a useful counterpoint to commercial mappings such as that of Pantone, or to more systematic mappings like Ridgway’s. In the sample presented in [fig:xkcdBlocks], we see a mix of naming metaphors. The usual food metaphors (melon, mocha, coffee) appear next to animal metaphors (camel, canary yellow) and creative compounds indicating a small amount of one color mixed into another (purplish, bluey, purply, preyish). The informality of the “-ish” suffix suggests extemporaneous description, as if colors are mixing in the imaginations of these survey respondants, in the absence of a ready-made metaphor. For comparison, greyish pink in this color map is blush in Pantone, and darkish green translates to online lime. And of course, one would expect that sickly green would not be an easily marketable name for a commodity, especially if it were food, so in Pantone the color is lime green. If an exact match for a hex value does not exist in a color map, I find the closest color to it using \(\Delta E^{*}_{ab}-76\). This is described in more detail in Categorization.
Summary and Comparison
| Abbreviation | Name | # Color/Word Pairs | Year | Weight |
|---|---|---|---|---|
| \(CM_S\) | Saccardo, Chromotaxia Seu Nomenclator Colorum | 500 | 1894 | 2 |
| \(CM_R\) | Ridgway, Color Standards and Color Nomenclature | 1113 | 1912 | 2 |
| \(CM_M\) | Maerz and Paul, Dictionary of Color | 3224 | 1930 | 2 |
| \(CM_P\) | Pantone Colors | 2310 | 2010? | 1 |
| \(CM_X\) | XKCD Color Survey | 954 | 2012 | 3 |
To compare the tendencies, or biases, of these color maps, and to better know how to balance them, I calculate the average of their 300-dimensional GloVe vectors (Stanford’s Global vectors for word representation, trained on English-language websites), and derived the cosine similarity to the vectors of a number of seed words:
\[similarity(\vec{A}, \vec{B}) = \frac{ \vec{A} \cdot \vec{B} }{\|\vec{A}\| \times \| \vec{B} \|}\]
Or, the dot product of the two vectors, normalized by the product of their two \(L_2\) (Euclidean) norms.
Fig. 5 shows a series of word vectors chosen to illustrate the vector similarity with the average vectors of each color map. \(CM_P\), the Pantone map, has a higher similarity to positive, marketable-sounding words, and words evoking leisure, whereas \(CM_X\) has a higher similarity for snot, a decidedly unmarketable word, discussed later, and Jaffer’s aggregation of \(CM_S\), \(CM_R\), and \(CM_M\) shows a slighly higher similarity for blood, also not a decidedly marketable term.
Given these tendencies, I weight these color maps as described in table 2, and combine them into one large master color mapping, which will become the basis of the imagination model below.
Deep Imagining: Color Inference
Mapping color expressions to hex values is only the beginning. Since explicit color words are not the only words that suggest color in the mind of the reader, and since broadly imagining a text will allow us to understand it more than narrowly, it would help to imagine those aspects of a text that are more difficult to imagine.
But this is a difficult problem: how can we derive the color of an object, or an adjective, where that color is known to a human reader, but not to a computer? For example, the words Statue of Liberty would recall the pale greenish color of copper oxide to those familiar with the statue, even from images, although this mapping isn’t readily available in a database.
If a poet or novelist presents us with imagery which is all of a single color, we want to be able to see that. One of the tasks of a literary critic, after all, is to be sensitive to the arrangements of the writer, so as to point out their resonances.
Take Katherine Mansfield’s 1922 story “The Garden Party,” for instance, an inspiration for Woolf’s Mrs Dalloway, and a classic modernist short story. (The short story collection of the same name is among the most colorful works, as ascertained in an analysis below.) Set on a fine day in early summer, in New Zealand, it is resplendent in greenery, which is to say, flora. But besides the grass, the lawn, the green bushes, the karaka-trees, and the leaves and stems of the flowers, there is an unusual abundance of other green things, as well. Laura’s sister Meg is wearing a “green turban” when she arrives for breakfast (Mansfield 287). A band plays music from a tennis court, which we might assume is green, since it’s compared to a pond, and tennis courts are usually green (ibid.). The band itself are wearing green, which makes Kitty compare them to frogs (294). Green baize doors separate the servants’ rooms from the rest of the house (289). Some of this is explicitly labeled as green, but some, like frogs and tennis courts, we are just expected to know are green things. While frog green does appear in one of the sources of \(CM_J\) sources, and tennis court green appears in some responses of the original \(CM_X\) survey, the value does not appear in the final mapping.
So to computationally imagine not just green itself, but things which are very likely to appear green, we need to find a way to imagine colors from any given word. This is where we must develop an engine to model deep imagination.
Word Proximity-based, \(M_P\)
One of the simplest methods of color inference is to calculate the syntactic distance from a known color word to a target word. Given a large enough corpus, it is quite likely that, for example, green will appear within several words of grass, and so by measuring the distances from green to grass, and noticing that these distances are much shorter than for the pair red and grass, we might infer that grass is green: that is, the literary imagination of grass has the color category green.
Another example might be inferring the color of a gull. Anyone who has visited the north Atlantic shore knows that gulls tend to be white and gray. Of the 170 times that the word gull appears in \(C_{PG}\), we see white appear within about ten words of it nineteen times. The lemma grey appears six times. Red, however, appears not once. Of course, both green and yellow appear twice, although not with the same relations in the dependency graph. Given this collocation data, we can write a model that guesses that gull is mostly white, a little gray, and with hints of green and yellow.
However, syntactic proximity is preferable to raw proximity itself, and so I developed an algorithm to score relations between two neighboring words, which uses both linear word distances and syntactic distances. I calculate syntactic distances by traversing the dependency trees of their containing sentences. By way of illustration, take these lines from Arthur Conan Doyle’s 1908 novel Sir Nigel:
Next morning they found themselves in a dangerous rock studded sea with a small island upon their starboard quarter. It was girdled with high granite cliffs of a reddish hue, and slopes of bright green grassland lay above them. (Doyle 250)
The syntax dependency graph of the clause, “slopes of bright green grassland lay above them” is parsed as shown in fig. 6
Here, bright and green are descendants—syntactic dependents—of grassland. This might even more accurately be parsed with bright and green together as one semantic unit.
This model infers color associations \(W_C\) from target words, \(W_T\), by traversing the syntax tree, and calculating weights accordingly.
However, modifiers are not always direct descendants of their modified words, since they might cross sentences. (Imagine a passage that were to read: “The slopes of grassland. How bright green they were!”) So to account for these types, I also compute weights based on the raw distances of these words from each other. The full algorithm is this:
- It begins by identifying a color word, \(W_C\) from color map \(CM_X\) in the target text.
- It then parses the containing sentence, and determines its syntactic dependencies.
- Starting from \(W_C\), it navigates through parent words and parent noun chunks \(W_T\) to the root of the sentence.
- If \(W_T\) is a noun or adjective, it is assigned a score: 2 if it is a direct parent of \(W_C\), or 1 if it is a grandparent of \(W_C\), at two steps’ removal in the syntactic tree.
- All other words nouns and adjectives are now candidate $WT$s, and are assigned a score: \(1/i\) where \(i\) is the distance, in number of tokens, from \(W_C\). Thus, it gets a score of 1 if it is a directly adjacent word, or 0.5 if it is two tokens away.
- These scores are then averaged for each token that shares the same lemma.
The resulting data structure looks like fig. 7, for grass:
There are plenty of colors, like blue, which are uncommon for grass (except for the Appalachain style of music). Yet these other colors represent only a small proportion of that for green. I blend these colors together, by finding their hex values in \(CM_X\), and then averaging their RGB values, after weighting them, so given RGB values \(R_i ... R_j\), \(G_i\), and \(B_i\), and weights \(W_i\), averaging them proportionally:
\[blended RGB = \frac{ W \sum_{R_i}^{R_j} }{ \sum_{i}^{j} W } , \frac{ W \sum_{G_i}^{G_j} }{ \sum_{i}^{j} W} , \frac{ W \sum_{B_i}^{B_j} }{\sum_{i}^{j} W}\]
Or, for all colors, weighting and summing each component of RGB space, and then dividing by the total sum of all weights. For grass above, we get #6b9c56, a pleasantly grassy color.
This model works reasonably well—that is, meets our modern English-speaking expectations of the archetypal colors of many nouns. But there are quite a few notable differences. One is the principle of color description exceptionalism I outline above: color descriptions are anomalies. Although sheep are typically white, and black sheep comparatively rare, the probability of encountering the phrase black sheep in British fiction is about five times greater than that of encountering white sheep, according to bigram data from \(C_{NG}\).
Fig. 8 shows a portion of the model’s inferences for sheep.
The blended color from \(CM_X\) is #5B5441, a dark greenish, and hardly the color one would expect of a sheep. Part of this is because, as you can see from the inferences above, \(CM_X\) contains many metaphoric colors like grass, heather, and stone. Although these are almost certainly not used as color words in their original contexts, this model counts them as colors. This is perhaps not a bug, however, but a feature of the program: by picking up on elements like stone and grass, we might fail to imagine the sheep itself, but we succeed in imagining the hue of its context, and in a sense, this is more information than we would get from simply imagining a sheep. And after all, when we ourselves imagine a sheep, we might very likely imagine it in its pastoral context, among grass and stones, rather than floating in a colorless void, away from everything else.
But the converse of that phenomenon is also present. We might not expect to see white and gray occur so often close to gull, because these are obvious descriptors, yet we do. When a writer wants to underscore the visual properties of an object that already has well-known color properties, this contributes to the impressionism of the piece: it allows the reader to see through the writer’s eyes.
This phenomenon is brilliantly at play in another Katherine Mansfield story, “Bliss,” written two years earlier in 1918. It is even more colorful than “The Garden Party,” and its colors much more variegated. This reflects the mood of its protagonist, Bertha, who is so happy that she is almost manic. Her attentions are everywhere, and glittering, reflecting off of everything.
There is a very particular quality of light in this story: it is not a categorical color, but a phenomenological one, describing not what Bertha knows the color to be, but how it seems. Bertha sees and feels bright sparks. The narrator tells us that “in her bosom there was still that bright glowing place—that shower of little sparks coming from it” (Mansfield 145). This moment is mirrored in a description of a fruit bowl which Mary at that moment brings in, and she perceives as “a glass bowl, and a blue dish, very lovely, with a strange sheen on it as though it had been dipped in milk.” Here, Mansfield contrasts the color category of the dish—it is blue—with its percepual reality: it appears white.
Bertha sees the fruit in this bowl also with an acute sense of newness: not quite estrangement, in Shklovsky’s formulation, but a newly intimate familiarization. “There were tangerines and apples stained with strawberry pink. Some yellow pears, smooth as silk; some white grapes covered with a silver bloom and a bug cluster of purple ones. These last she had bought to tone in with the new dining-room carpet” (146).
“Apples stained with strawberry pink,” is an uncommon metaphor for apples, which \(M_P\) models as in fig. 9, and for which the blended color is #AC883B.
In other words, apples are usually golden, green, red, gold, or yellow, but rarely strawberry pink. (There is an apple cultivar called the “pink lady,” but it wasn’t cultivated until 1976.) As with milk, this is a food metaphor, one which uses these colors, as the foods themselves seem to do, as a marker for something delicious or desirable.
Grapes, too, typically belong to the categories red or white, like their wine. Yet white grapes, and white wine, are not white at all, but a pale green.Anders Steinvall points this out, noting that the white of white wine is an instance of type modification: (Steinvall 57)
Red grapes and red wine are not red, either, but are usually a deep purple. This illustrates the lemon-yellow position I have outlined earlier: color designations are more a matter of convention than faithful representation of the visual world. Mansfield would have been aware of this quality of grapes, and has Bertha call them “purple” to show that she is attune to the phenomenon of their hue. Mansfield further highlights this by showing the chromatic harmony Bertha expected, and sees, between the grapes and the carpet.
So by modeling imagination in this way, we’re able to take advantage of the ways writers show us the perceptual experience of words, rather than just their semantic categories. But this model is only one component of a larger system.
Dictionary-based inference, \(M_D\)
To short-circut the problem of color exceptionalism, I mine color inference data from more straightforward definitions, like those found in dictionaries and encyclopedias. There, sheep are more likely to be described as white. Project Gutenberg provides a copy of Chambers’s Twentieth Century Dictionary of the English Language, published in London in 1908. If I did not already have color mappings for grass (\(CM_X\): grassy green, #419c03; grass, #5cac2d; grass green, #3f9b0b), we would find this entry for grassy: “covered with or resembling grass, green.” Since words and their definitions are regularly formatted in this dictionary, it becomes possible to parse the dictionary entry into word/definition pair, and load them into a lookup table. From there, I construct an graph, where nodes are dictionary words and their colors, and edges accrue weight each time a color word from the color maps appears in the definition of one of the defined words. Thus, if we see grassy appear on the left of the page, and green in its definition, we give it a score of one. If green were to appear twice in its definition, we’d give it a score of two, and so forth. I repeat this process with an encyclopedia: The Nuttall Encyclopædia, first published in London in 1900, and still in print by 1966. Other dictionary and encyclopedia-like works are available in the usual plain-text sources, but few meet the criteria of (a) British, (b) out-of-copyright, (c) in plain-text, and (d) in an easily parsable format.
This model does not seem to perform as well as \(M_P\), however. Here is an entry for grass, for example.
The model triangulates between the color word and entry/definition pairs, but only finds a few single instances each. Possibly as a happy coincidence, however, the resulting aggregate here is #89bc6e, a very grassy color.
When merging these models, I weight \(M_D\) the lowest, since with a lack of data (dictionaries), and an inconsistent level of descriptive detail, it’s less reliable.
Image-based: \(M_I\)
A third solution mitigates some of the problems described above, by escaping comparison between colors and objects, and looking to images as a source of color information. Given a database of images that are correctly labeled, it should be possible to extract color values from those images, and build a pipeline that infers a color given a word. Lucily, two somewhat newly-created web services provide such a database: Unsplash and Pexels are stores of open-licensed stock photos and illustrations, which provide open APIs (Application Programming Interfaces) which allow users to retrieve images based on a given keyword. This follows methods used by other projects that attempt to map words and colors (Guilbeault et al.).
However, an image of a given object, such as a sheep, rarely contains only sheep. There is almost always a background to the image which is of a different color. I try to control for this difficulty by disregarding the most frequent color of these images, and only working with the second and lesser frequent colors. From there, I average the resulting colors of all the images, to retreive an imagined (inferred) hue for the lemma.
The resulting algorithm may be summarized as follows:
- Scan through the text, looking for nouns, adjectives, or similar words (words with potential visual content).
- For each matching word, find its lemma.
- Query the Pexels / Unsplash APIs, giving the lemma as a search term, and ask for ten image addresses. Download them.
- For each downloaded image, find the second to Nth most frequent color.
- Average all these colors proportionally, using the algorithm described above.
- Return a pairing of a word with a corresponding RGB hex value.
Fig. 10 is a sample of the model’s inferences for three fairly concrete words, wheat, butterfly, and tennis, and with three more abstract words, deposit, pure, and travelling. I’ve included a few images used in the creation of each color, to allow for some model introspection.
Wheat the model predicts reasonably well: the resulting color is a golden brown. Butterfly is more uncertain, in part because there are a very large number of butterfly species, and so there is an incredible diversity of color patterns between them. Tennis one might have expected to be more greenish, since most tennis courts are green. The averaged color here reflects the inclusion of the image where the court is red, and the image of the racket alone. Deposit is curious: probably because it would be a rare word to use to tag a photo, the first four images are likely from the same photographer and shoot. The last is an image of bars of gold, however. This is interesting, since it is not a stack of paper money, but of an archetypal idea of money—the gold standard—which is no longer in widespread use. The inferred color for pure, interestingly, is anything but—it is a dirty grayish. Even though most of these images are water-related, one is of honey. This seems to show one of the pitfalls of computationally imagining an abstract concept. Travelling, on the other hand, is a very distinct blue, owing to the color of the beach scenes that have become emblematic of travel today. Coincidentally, however, blue seas would have been a common feature of international travel in the early twentieth century.
A broader view of this model, shown below, gives one a sense of a few trends. First, almost all of these colors are very desaturated: they appear as if they began as pure pigments, and were mixed with a titanium white. This is owing to the way each of these is by nature an average of several colors: they are mixed. At a glance, the legal-sounding words power and possession appear darkest. The two words dealing with negative emotion, rudeness and objection, appear redder than the others. And words that evoke comfort, reliance, liberty, and amusement, appear bluish. Neighbour and neighbourhood, meanwhile, appear green, probably owing to stock images that involve green lawns.
When projected in HSL space, certain patterns emerge, as well, as shown in fig. 11.
For the problems cited above, I weigh this model higher than \(M_D\) but still lower than \(M_P\). In the absence of an evaluation metric—and I doubt one is possible—I feel like our intuition as readers, and imaginers, is enough to evaluate and weigh these methods of imagining.
Comparison
| Model | Inference Basis | Weight |
|---|---|---|
| \(M_P\) | Literary Proximity | 1 |
| \(M_D\) | Dictionary Proximity | 0.6 |
| \(M_I\) | Image Aggregation | 0.8 |
I combine these three models according the weights given in table 3, and produce a master mapping I’m calling a deep imaginer. This I then integrate with the shallow color maps described above, cascading all the models together.
Named Entity Recognition (NER)
The Rose Problem
Early iterations of this engine found a staggering incidence of the color word rose in the literature of this period. It didn’t take much introspection to determine that this was not exclusively the color rose, but either a woman’s given name, Rose (my parser is case-insensitive), an equivalent surname, or the past tense of the verb rise. One could remove all tokens where the first letter is capitalized, but then that would also eliminate the color rose which appears at the beginning of a sentence. One could run a part-of-speech tagger over the text, and throw out all the verbs, but this only solves half the problem.
A method exists which, if properly extended, mitigates this problem. Named Entity Recognition, or NER, is a sub-field of natural language processing which computes the probability that a string of words is a named entity. Usually, these are people, places, organizations, languages, ethnicities, and so on, and detecting them has been useful in commercial applications of text analysis, where a corporation might be interested in finding all instances of Apple, the computer company, but ignore all instances of apple, the fruit.
NER has been practiced, in one form or another, since at least Lisa Rau’s 1991 paper, Extracting Company Names from Text (Rau). But whereas early techniques like Rau’s were heuristic, modern methods use computational neural networks to achieve this end. Among the most accurate NER engines now is Explosion AI’s SpaCy library, which uses residual convolutional neural networks along with specialized word embeddings to achieve reasonably accurate predictions of entities like personal names, organization names, as well as disambiguate numeric tokens into quantities, cardinal numbers, and so on (Honnibal).
NER becomes useful to my study in two ways: first, it allows me to discard those entities like “Rose,” (a given name), as well as “Mrs. Brown,” and “Mr. Green.” (Although there is a case to be made that the writer’s choices of these names, where they are fictional, are as deliberate as choice as a visual descriptor.) Second, since this general-purpose NER doesn’t detect color expressions, descriptions, or any literary features, I train it to do so.
I train two NER models, which I’ll use to detect text with visual properties: a shallow model, \(NER_S\), and a deep model \(NER_D\). The seed data set for the more restricted model, \(NER_S\), is trained on raw color entities from \(CM_X\), while that for \(NER_D\) is trained on the set of more general visual expressions spans generated from the deep imaginer.
The Beret Test
These are not the only training data sets I feed to the model, however. I use SpaCy’s Prodigy tool to dynamically correct the model’s most uncertain guesses, across a corpus of edge-cases. In training \(NER_S\), I aim for a more restricted definition of a color: one which is parsable as a color without much context. For example, in the famous hook from the Prince song, “Raspberry Beret,” we understand that in the words “she wore a raspberry beret,” “raspberry” is the color of her beret, which is made of cloth, and not of one or more raspberries. But if we replace raspberry with a more uncommon color, say, electric, or cyberspace (color names from \(CM_P\)), it is not still clear that the word is a color description, and does not describe some other attribute.
The process of training this model was instructive. \(NER_S\) found instances of the word salmon often, and since the word appears in nearly every color map (e.g., \(CM_R\): #D9A6A9; \(CM_X\): #FF796C), it identifies any use of salmon, regardless of whether it refers to a color. It is important to note here that salmon, the color, refers to the color of salmon meat, rather than the color of the fish itself. In other words, the color refers to the inside of the fish, rather than the outside. Disambiguating between these two senses of the word is a non-trivial task for this model. Nonetheless, given a large number of training examples, the model is able to perform better than chance.
What we are left with, after training, are probabilistic models capable of identifying explicit color expressions, in the case of \(NER_S\), and anything that may be imagined, in the case of \(NER_D\). So, to employ Mansfield again, \(NER_S\) finds instances of green baize, and assigns it an RGB value, whereas \(NER_D\) finds instances of tennis lawn, and also assigns it a green value (following color inference, described below).
But first, we would need a way of categorizing the color values generated by these models. Much in the same way that a text analysis project needs a lemmatization system to group together words like sky and skies, go and went, we need a way to bring together variations, shades, and other common properties of colors.
Categorization
Color Spaces and Color Difference
We now move from categorical description of color to quantitative—i.e., from that is blue to that is 80% blue. Now that we’ve mapped color words and other color-containing text to the model’s guesses of their mappings, we need a way to organize them, in relation to one another, and in relation to our perception of them. This is problematic, since we are dealing with two ontological domains: a linguistic domain, and an analogous psycho-physical. Furthermore, there are a multitude of ways to quantify color properties, and to organize colors by those properties.
Relations between colors—color difference—is a long-standing problem. Colors are typically categorized in relation to one another by embedding them in a color space: a vector space in which each color is a point in its coordinate system. These spaces are very precisely described in technical literature (Fairchild, for instance). The biggest problem they attempt to solve is that hues themselves, due to the anatomy of the eye, do not have linear relationships—this is why three-dimensional projections of these color spaces are often conical, or even asymmetrical. Furthermore, each color space must account for ocular physiology across individuals; differences in ambient reflectors, illuminants, and other lighting conditions; and differences in reference points (white values used as anchors for other color properties). We will not need all of these details, but a summary of these color spaces is necessary, since I will be using many of them below.
It is useful to pause for a moment, however, and consider that color spaces are themselves descriptions, in the literary sense, of color—they can approximate their object, but only asymptotically. And yet so much of our modern world is made up of these approximations. Every screen we use—including the one I’m using to type this now, and the one you’re using to read it—is composed of millions of tiny picture elements—pixels—each with three lights: a red, a green, and a blue. The more specific colors, and the images, that we see on these screens are only mixtures of those elements.
The most common color spaces in use today include RGB, which stands for red, green, and blue; CMYK, or cyan, magenta, yellow, and white; HSL, or hue, saturation, and luminosity; and CIELAB, the newest and most accurate of these spaces. RGB is most common among light-producing devices like computer monitors, and generates colors additively, by mixing red, green, and blue light. These values are often expressed in hexidecimal, with the marker #, such that #ff0000, red, indicates the highest value for red (ff), along with the lowest value for green (00), and the lowest value for blue (00). CMYK is the most common for print media, on the other hand, since it describes colors subtractively, combining cyan, magenta, and yellow. HSL is a useful derivative of RGB, meanwhile, which allows for numeric manipulation of colors according to these values of hue, saturation, and luminosity.
The current standard colorspace, CIE \(L^* a^* b^*\), usually abbreviated CIELAB, is a product of a century’s long effort by the Commission Internationale de l’Eclairage [International Commission on Illumination], or CIE, an organization formed in 1913 to solve problems of chromaticity standardization, among others. A 1973 meeting of the CIE Colorimetry Committee, having evaluated a number of previously used color difference formulae, produced the first iteration of the LAB colorspace, intended to model human color perception. Here, \(L^*\) represents luminosity, \(a^*\) represents a spectrum of hues between green and magenta, and \(b^*\) represents hues between blue and yellow.
Relations between colors may then be calculated with respect to this coordinate system. The Euclidean distance between two colors in a LAB vector is therefore the square root of the differences of each of its components. The CIE calls this formula \(\Delta E\) (Robertson 167).
\[\Delta E = \sqrt{(\Delta L^*)^2 + (\Delta a^*)^2 + (\Delta b^*)^2}\]
Since CIELAB space best represents human perception of color, I’ll use it wherever possible, and calculate color distances using \(\Delta E\).I implement this function here, in the color categorization module of my color analyzer.
However, I have to translate frequently between LAB space and RGB space, since most of the color maps I’ve derived, are either scanned using digital photography, or, in the case of the XKCD map, produced using computer monitors.
Debates in Color Categories and Nomenclature
If we aim to quantify the occurrences of certain color concepts, and not just the color words, then there must be a way to categorize visual experiences. For instance, if we encounter the expression light blue, we must be able to categorize this as a variety of blue, or else we will need to process and compare thousands of variables, instead of just a few. Yet the epistemological problems of the color/word interchange make this a difficult task. To begin with, since we are dealing with spectra, the boundaries of these categories are not well-defined. But the very existence of the categories themselves should not be assumed, either. While, to a painter or interior designer, the differences between ecru and eggshell may be crucial, these words may not be in the working vocabularies of some novelists. I say “working” here because they might be recognizable, and even familiar, to a writer, but they might not be the operative metaphors he or she chooses when describing a scene, or allowing a literary persona to describe it. So the color spectrum of a writer’s idiolect is always a subset of his or her dialect.
For instance, we might consider light blue to be a subcategory of blue, since the word blue is contained within it. However, is pink necessarily its own category, or is it simply a shade of red? And if so, is light pink a subcategory of pink or of red? We might categorize these colors differently if we were to use the hues rather than their written expressions.
We might look to other languages to see how these concepts are expressed, and learn about our own by comparison. Some languages lack a monolexemic term for pink, and others still have additional pink-like lexemes in other hue spectra. The Russian language, for instance, has the color-categories, or monolexemic color terms, синий [sínij], usually translated as “blue” or “dark blue,” and Голубо́й [golubój] which we might gloss as “light blue,” or “sky blue.” The image-based color mapping model, described below, predicts similar, but not identical colors for these English and Russian words, as well as their most common French translations:
| Russian | Ru.RGB | English | En.RGB | French | Fr.RGB |
|---|---|---|---|---|---|
| синий | #163B97 | blue | #1A5AB6 | bleu | #0C4397 |
| Голубо́ | #75A7CD | light blue | #83CFE8 | bleu claire | #8DC7D9 |
Semantically and chromatically, these color categories are not synonymous. Just in the way that every translation requires some compromise, some reshaping, colors do not always cleanly map across languages. Some do: English blue and French bleu, as etymological kin, are not only morphologically closer than the English/Russian pair, but semantically, as well, and the model predicts this kinship.
The differences in color terminology between languages are important for us to bear in mind, even when the primary analysis below deals only with texts in English, because these differences are analogues for the gaps, and communications, between language and vision. Furthermore, most of the writers I’ll be discussing here speak more than one language: either from birth, as with Conrad and his native Polish, or through study, as with James Joyce, who was fluent in at least five languages. And some experiments in psychology show semantic shifts in color categorization among speakers of more than one language (Ervin; Caskey-Sirmons and Hickerson; Athanasopoulos et al.).
More importantly, however, in order to categorize color words, we must first decide what our base color categories will be. This is no easy matter, and has long been the subject of debate. By comparing languages, linguists have often tried to ascertain what fundamental colors are, irrespective of their respective cultures.
One side of this debate calls into question the basis of fundamental colors, instead positing that color nomenclature, along with other phenomena, is in fact a cultural or linguistic construct. Probably the most well-known of these theories of linguistic relativism is that independently promoted, starting around the 1930s, by linguists Edward Sapir and Benjamin Whorf. Whorf’s 1940 summary of this view puts it succinctly: “the categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face. On the contrary the world is presented in a kaleidoscopic flux of impressions which have to be organized in our minds. This means, largely, by the linguistic system in our minds” (Whorf 212).
On the other side of the debate, usually termed universalism, is an influential study of cross-linguistic color terminology, in a 1969 monograph of Brent Berlin and Paul Kay, Basic Color Terms: Their Universality and Evolution (Berlin and Kay 2). In particular, they name eleven categories: “white, black, red, green, yellow, blue, brown, purple, pink, orange, and grey,” and suggest that these categories develop in roughly that order—that all languages have words for white and black, that if they have a third, it is red, and so on. Graphically, Berlin and Kay present this sequence as in the following diagram, where languages that have red must have both white and black, and so on. There is no order between yellow and green, but languages that develop a word for green would then develop a word for yellow, and vice-versa.
\[[\substack{white \\ black }] < [red] < [\substack{green \\ yellow}] < [blue] < [brown] < [\substack{purple \\ pink \\ orange \\ grey}]\]
Berlin and Kay see this sequence as a linguistic evolution in more than one sense—a dangerous term, in that it suggests a linear progression of simple to complex languages. The reasons they give for this are “increasing technological and cultural advancement” among the languages they compare. By way of explanation, they suppose that,
… to a group whose members have frequent occasion to contrast fine shades of leaf color and who possess no dyed fabrics, color-coded electrical wires, and so forth, it may not be worthwhile to rote-learn labels for gross perceptual discriminations such as green/blue, despite the psychophysical salience of such contrasts. (Berlin and Kay 16)
While the contrasts might have a physical basis, their linguistic categories do not map evenly to them, as Berlin and Kay themselves show. And as one might predicted, since 1969, their arguments of universal categories—and to a larger extent those of language evolution—have been either denounced as Anglocentric, or at least treated with a healthy skepticism. For instance, in 2006, Anna Wierzbicka argues that even the notion of color itself is not universal. Citing decades of research within the subfield of Natural Semantic Metalanguage, Wierzbicka argues that, “while many languages do not have a word for ‘colour,’ all languages have a word for seeing,’” and that “it makes more sense to ask about the universals of seeing rather than any putative ‘universals of colour’” (Wierzbicka 3).
I take no sides in this debate, but present it as evidence of the bond between language and perception. While there are few hard-line Whorfians remaining in linguistics, or universalists, both camps seem to agree that there are exceptions pulling at their theoretical sweaters, and colors are frequently the axis along which that pulling happens.
Is blood red?
To further complicate our conception of color/word translation, let’s return to the discussion of color word conventionality begun in the section on lemon-yellow above. As I have argued, although red is the conventional color of blood, blood itself is rarely red. This phenomenon presents itself in process of computational color categorization attempted here. While categorizing colors using CIELAB \(\Delta E\), which model human perception, I find that the category for the \(CM_X\) color word blood (#770001) gets categorized as brown, instead of red, as one might have predicted. Incidentally, blood red (#980002) is an entirely different color in the \(CM_X\), which is redder (i.e., contains a higher R value in its RGB representation) than blood. And dried blood (#4b0101) also exists, and is mapped to a darker red.
My initial feeling was that blood was miscategorized as a brown, and should instead be categorized as red. We all know blood is red–the term blood red itself proves it, right? But to look through images of blood, we may, in fact, discover that it is not red, but at best, a reddish brown. This is seemingly confirmed by the deep imaginer’s image-based imagined color (described below), which is #915b47. An image search at a stock photo provider like Unsplash or Pexels seems to confirm this, as well. However, crucially, the same searches for illustrations, rather than photos, depict blood as a bright red, instead of reddish brown—this seems to show that the linguistic-cognitive concept of blood is aligned with the concept of red, even though they aren’t visually equivalent. So when the OED editors, however meticulously they document the usages of blood-red, which date back to early Old English, gloss the term disappointingly literally as “red like blood; blood-coloured,” they do not account for the discrepancy between the color of “blood-red” and the actual color of blood (“Blood-Red, Adj.”).
In British literature of this period, blood-red is often used to evoke other qualities of blood itself, although not necessarily its true color. In the hell-sermon that is the pivotal scene in Joyce’s A Portrait of the Artist as a Young Man, it is used to underscore the apocalyptic scene that Father Arnall is trying to describe: “the doomsday was at hand. The stars of heaven were falling upon the earth … The sun, … had become as sackcloth of hair. The moon was bloodred” (Joyce et al. 99). Lunar eclipses, in which the sun’s light on the moon is eclipsed, leaving only the earth’s light, make the moon appear dark red. These have long been described in English as a blood moon, but this is not just a color comparison: it is a metaphor which anthropomorphizes the moon in this state, comparing the moon’s face to one whose face has filled with blood, out of anger or another heightened emotional state. Father Arnall’s use of this metaphor, along with his simile for the sun, anthropomorphize heaven as a way to dramatize the wrath of God.
In Thomas Hardy’s Tess of the d’Urbervilles, Tess is described, in an early foreshadowing scene, as “not divining” that Alec d’Urberville, “one who stood fair to be the blood-red ray in the spectrum of her young life,” would come to be “the tragic mischief of her drama” (Hardy 73). As in Joyce, “blood-red” allows for polysemy. First, it is “red … in the spectrum of her life”: red is the first, highest-frequency, and longest-wavelength band of a prismatic or spectrographic projection of Tess’s life, which implies that Alec will be for her among the first and most striking bands of her life. Spectroscopy—a kind of scientific “divining” of the material composition of matter, based on the spectral composition of its light—had come of age as a science in the 1870s and 80s, only a decade or two before Tess’s publication.
Second, “blood red” here implies a more literal red which comes from blood: a blushing which is seen in human faces, as well as, by extension of the metaphor, flowers, and fruit. This is the culmination of a chapter’s worth of red imagery, since Tess and Alec have just been picking strawberries and roses, and it is intertwined with imagery of Tess’s coming-of-age, or blossoming as the floral metaphor often has it.
When blood-red is understood as blushing, however, Berlin and Kay note that many words for red are derived from blood (Berlin and Kay 38). Yet this is not always the color of external, disembodied blood, which we have already established is more akin to brown, but often refers to pinkish, blood-rich skin. In the Hungarian language, to choose one cross-cultural example, there are famously two words for red, vörös, derived from the word for blood, and piros, of similar etymology, but referring instead to, as Wierzbicka posits, “the color of blood inside a person’s body (visible sometimes in an open wound and in a person’s ‘red’ face)” (Wierzbicka). A red face, Wierzbicka suggests, is not an attempt at accurately describing the color of someone’s face, but only that it has become more pink, i.e., taken on a more reddish hue than before. The red in question, then, is more of a reference to the concept of red, via blood and blood-red, than to the color phenomenon itself.
This red—again, not really the color red, but the concept—is the same red of rouge, the cosmetic used to emulate blushing, and whose name is derived from the French word for red. Rouge itself is often not red, but a somewhat reddish, pinkish, or purplish tint of another color. Max Beerbohm famously sings the praises of rouge, as a symbol of colorfulness and artifice, in an 1894 polemic in the inaugural issue of the short-lived but highly influential aestheticist journal bearing the name of another bright color: The Yellow Book. “The Pervasion of Rouge,” originally titled “A Defence of Cosmetics,” declares the end of the Victorian era, and thus “sancta simplicitas,” which we might interpret as a restricted color palette (Beardsley 75). Queen Victoria would not die, taking her eponymous era with her, for another seven years, but this declaration is an important herald of the “bright modernity” to come, as Blasszczyk and Spiekermann term it (Blaszczyk and Spiekermann).
P.A. Saccardo’s taxonomy does not place the color of blood with red at all, however, but with purple: he gives sanguineus as a Latin synonym of purpureus, along with the Greco-latin hæmatochrous, hæmatinus, and hæmatites (Saccardo 8). This is the traditional categorization of classical antiquity: the mapping appears in Homer, where in the Iliad, the earth is wet with purple blood. A. T. Murray’s English translation of Homer gives “thus mighty Aias charged them, and the earth grew wet with dark blood,” [αἵματι δὲ χθὼν δεύετο πορφυρέῳ] although πορφυρέῳ, which is translated as dark, is an etymological ancestor of purple (Homer). This categorization continues through Vergil, Ovid, and Horace. In fact, as Jacquiline Clarke points out, Horace plays with the traditional Homeric association of πορφύρεος with the sea and with death (πορφύρεος θάνατος, purple death or dark death, appears thrice in the Iliad), by juxtaposing the two in a purple blood-stained sea (Clarke 132). However, Liddell and Scott are quick to warn that “Homer seems not to have known the πορφύρα, [a purple fish, or purple dye] so that the word does not imply any definite colour” (Liddell and Scott).
To further complicate matters, Saccardo’s purpureus, while certainly on a spectrum that seems to range from red, to purple, and finally to brown, has a color of #8D0202, at least as it appears in the scanned edition from archive.org, however faded its original pigments may be. Some may rightly call this color red. So blood is not really red; it’s purple. But purple is red.
We may add blood to the long list of things called red which aren’t: the Red Sea (it’s blue), red wine and red grapes (they’re purple), red hair, red pandas, and Mars, the red planet (they’re orange). It comes as no surprise to report that red hair appears close to 900 times in \(C_{PG}\), but that orange hair, ginger hair, and copper hair are used only thrice each. Or that red wine appears 160 times, but purple wine only thrice. These are simply English-language conventions. But they prove that we must be especially careful while modeling our imagination of these terms. When we read red wine, do we imagine something red, or purple?
Similarly, when we read red hair, are we imagining red? The persistence of the villainous red-haired minor character trope in sensational literature of this period is evidence that the associations we’ve so far catalogued for red—blood, violence, ferocity, and so on—seep subconsciously into the characters’ depictions in fiction.Depictions of red-haired people in fiction could easily be the subject of another chapter, but are a little too far afield for this one. A concordance of \(C_{PG}\) for red-haired and similar terms, however, shows a multitude of unflattering accompanying personal descriptions.
We do not see those same stereotyped character attributes among more conscious and nuanced descriptions of hair color.
I want to reiterate here that these difficulties of textual color are not, as they may seem, merely background linguistic components of a literary art that is unconscious of them. Rather, they are fundamental to the process of literary description. Some writers are more explicit than others about these optical mechanics. But the modernist writers I choose to study the deepest in this chapter foreground color epistemologies in a way that, while it may not be a new literary device, is stronger, and brighter, and more variegated than before.
The wine-dark sea
Among the writers that deal most explicitly with color is James Joyce, an author I return to frequently. The first scene of Ulysses introduces a motif that recurs throughout the novel: the color of the sea. There, Buck Mulligan is gazing out onto the Irish sea from the crenellated parapets of Martello tower, in Sandycove, south of Dublin, and musing at once irreverently and reverently:
God! he said quietly. Isn’t the sea what Algy calls it: a great sweet mother? The snotgreen sea. The scrotumtightening sea. Epi oinopa ponton. Ah, Dedalus, the Greeks! I must teach you. You must read them in the original. Thalatta! Thalatta! She is our great sweet mother. Come and look. (Joyce, Ulysses 2)
How is the sea “snotgreen?” \(CM_X\) contains several colors for sea, as shown in table 4 below, as well as two mappings for snot. (Snot is not present in other color maps—unsurprisingly, since it would not very likely be a marketable name for a paint.)
| \(CM_X\) Name | RGB Hex |
|---|---|
| bright sea green | #05ffa6 |
| dark sea green | #11875d |
| deep sea blue | #015482 |
| light sea green | #98f6b0 |
| sea | #3c9992 |
| sea blue | #047495 |
| sea green | #53fca1 |
| snot | #acbb0d |
| snot green | #9dc100 |
And this list does not even include the many seafoam and seaweed variations. The variety of sea-like colors is an interesting problem, because seas themselves have a very wide range of colors among them, and even within any given sea. As suggested here in the name deep sea blue, the depth of the sea changes its apparent color. For comparison, the image-based color model predicts #98B8B3 for irish sea —a somewhat snot-green color.
Epi oinopa ponton, according to Don Gifford’s notes for Ulysses, is Homeric Greek for “upon the wine-dark sea,” a classic Homeric epithet that occurs throughout The Odyssey (Gifford and Seidman 15). It has long been a puzzle of Homeric scholarship as to why the sea is not blue, or green, but “wine-dark.” We should remember, however, that “dark” is an artifact of this translation convention, for in the Greek, which Mulligan advisedly does not gloss, “ἐπὶ οἴνοπα πόντον” might also be rendered “over the vinaceous sea” or “over the wine-like sea,” since οἴνοπα itself, despite clearly being used as a visual metaphor elsewhere in Homer, does not explicitly contain a signifier for dark, which would be closer to μέλας in Homeric Greek—in fact, elsewhere in Homer, wine itself is described as μέλας, although not here (Gladstone 472; Liddell and Scott[[http://www.perseus.tufts.edu/hopper/morph?l=oi%29%2Fnopa&la=greek&can=oi%29%2Fnopa0&prior=e)pi%5C&d=Perseus:text:1999.01.0135:book%3D1:card%3D178&i=1#lexicon)[]]].
One of the more well-known works of scholarship on this topic, however dated it may be considered now, is that put forth in William Gladstone’s 1858 Studies on Homer and the Homeric Age. Although better known as the four-term prime minister of the United Kingdom, discontinuously from 1868 to 1894, Gladstone was a Homeric scholar of some distinction, and among the more interesting theses of this work is his catalog and interpretation of color words in the Homeric epics.
After a thorough concordance of visual terminology in Homer—which what one might call an analogue quantitative literary analysis—Gladstone concludes that Homer’s color expressions are relatively few. He lists as Homer’s only color words—excepting color metaphors—as λευκός (white), μέλας (black), ξανθός (yellow), έρυθρός (red), πορφύρεος (violet), κυάνεος (indigo), φοίνιξ (a phoenix, or Phoenician, purple or indigo), and πόλιος, (gray, grizzled) (Gladstone 459). His color metaphors, though, number thirteen, among which is οἴνοπα, vinaceous. Gladstone notes that Homer applies οἴνοψ to only two objects, oxen and the sea. This puzzles him, however, since:
… there is no small difficulty in combining these two uses by reference to the idea of a common colour. The sea is blue, grey, or green. Oxen are black, bay, or brown. … It is remarkable that, among colours properly so called, Homer has none whatever, derived from the name of an object, that are light, unless it be in the case of the rose. (Gladstone 472)
οἴνοπα functions just as πορφύρεος does: as a visual descriptor of the sea, in the sense of “blood-red”: by comparing the sea to wine, it is not just the color that is compared, but other aspects, as well. We might imagine a tumultuous sea, for instance, which causes the ships upon it to sway as if drunken, as in Arthur Rimbaud’s poem “Le bateau ivre.” This same motion of the sea might also cause sailors on it to vomit as if they’d had too much wine.
The blood/wine/sea metaphoric trinity was not lost on Joyce, either: in the “Proteus” episode of Ulysses, we see Stephen daydream the following, looking again out at the Irish sea:
A tide westering, moondrawn, in her wake. Tides, myriadislanded, within her, blood not mine, oinopa ponton, a winedark sea. Behold the handmaid of the moon. In sleep the wet sign calls her hour, bids her rise. Bridebed, childbed, bed of death, ghostcandled. Omnis caro ad te veniet. He comes, pale vampire, through storm his eyes, his bat sails bloodying the sea, mouth to her mouth’s kiss.
Here, Stephen’s poetically free-associating imagination conjures a nighttime sea as “the handmaid of the moon,” because it is “pulled” by it in its tides. He extends this feminine analogy, via the conventional euphemism for menstruation, to a series of blood-soaked bedsheets, with their analogue in the bloodied sea, and a recollection of a sexual episode with a prostitute that Stephen will remember more fully later. A common connection in this stream—or sea?—of consciousness is the purple color. Color is, here and elsewhere in Ulysses, the fulcrum of poetic associations which align along a visual axis.
What’s important to recall here is that this purple is closer to how the sea appears than how it is categorized. Again, conventional associations have it that the sea is blue, and that blood is red, and that red wine is of course red, but to read Homeric descriptions of the sea and blood and wine as purple, we are more reminded of the perceptual phenomenon than the linguistic category. And that, I argue, is a strong force in the literary description of this period.
Imagining Texts: Aggregating Color Mappings
So far, we have encoded color mappings \(CM\), which feed into color inference models \(M\), which in turn feed into named entity recognition models \(NER\), as illustrated in fig. 12 below.
I now derive the model’s best guesses for color spans in a number of corpora, while collecting metadata: the title and author of the text, its publication date, its category or library subject heading, and a number of other data. I then use these models to infer color information about large collections of texts, according to groups of that metadata.
How does color change across literary history?
In Virginia Woolf’s meta-essay, “The Decay of Essay Writing,” she claims that her moment in history “has painted itself more faithfully than any other in a myriad of clever and conscientious though not supremely great works of fiction; it has tried seriously to liven the faded colours of bygone ages” (Woolf and Bradshaw). While she does not use “colours” exclusively in its literal sense, there is a pervasive sense that modernity is brighter, and more colorful, than the previous age. This is the thesis of Blaszcyk and Spiekermann’s Bright Modernity, which shows how, owing to various material factors including the newly widespread availability of synthetic pigments, early twentieth century culture was much more colorful—literally—than that of the previous century (Blaszczyk and Spiekermann). The first of my analyses here puts this hypothesis to the test, inferring colors from \(C_{PG}\), in order to answer the question of whether twentieth century writers are more colorful, or more descriptive, than their predecessors.
One of the drawbacks of \(C_{PG}\) is that original publication dates are missing from the metadata. It would be best to compare the dates of original publication for each text with the total proportions of color spans found in each. But even though I was able to fill in much of this data, by querying several book metadata APIs, much of it remained missing. The author’s date of birth is present in \(C_{PG}\), however, and so fig. 2 shows the correlation between the author’s date of birth and the total proportions of detected color expressions in that author’s text.
Using an author’s date of birth as a proxy for date of publication is not ideal, but this picture doesn’t change much, however, when using what few publication dates are available: a linear regression of those points—even though this represents a subset of the total corpus—still shows an upward trend, however weaker: see fig. 1. In this case, each point represents a single novel or collection of poems.
In both cases, we can see an obvious trend: British fiction and poetry becomes more colorful over this literary period.
Which subjects and genres are most colorful?
It would make quick work of this study if it turned out that almost all highly colorful works in this corpus come from a single genre, like painters’ romances. So by grouping these text by subject or genre, I can begin to see trends among colorful texts. I first examine correlations between texts’ Library of Congress Subject Headings, which are metadata values present across \(C_{PG}\), and the number of different unique colors the model detects. This is not measuring the number of colors, but their breadth: the creativity with which writers relate their visual domains. Table 5 below shows this correlation.
| Library of Congress Subject Heading | # Unique Colors |
|---|---|
| Love stories | 264 |
| Short stories, English | 215 |
| Psychological fiction | 206 |
| Detective and mystery stories | 206 |
| London (England) – Fiction | 203 |
| Adventure stories | 203 |
| England – Fiction | 196 |
| World War, 1914-1918 – Fiction | 193 |
| Domestic fiction | 192 |
| Short stories | 187 |
| Man-woman relationships – Fiction | 185 |
| Science fiction | 180 |
| Fantasy fiction | 176 |
| Historical fiction | 174 |
| England – Social life and customs – 19th century – Fiction | 171 |
| Sea stories | 166 |
| Young women – Fiction | 152 |
| English fiction – 19th century | 149 |
| Private investigators – England – Fiction | 118 |
The LCSH love stories has the highest number of unique colors in this corpus, by far. This is a large category of mostly novels, containing a number of well-known works. Among these are three novels by D.H. Lawrence, three by Wells (Marriage, Ann Veronica and Love and Mr. Lewisham), and two by Woolf (The Voyage Out and Night and Day).
Love stories are colorful because time (durée in Bergsonian formulation) is softer in love stories. The French literary theorist Philippe Hamon observes that literary descriptions tend to happen when the describing character is “‘absorbed,’ ‘fascinated,’ ‘loses track of time,’ because of what he is looking at,” traits that would apply equally as well to lovers (Hamon 149). The describer “has been able to abstract himself for a while from the plot; the ‘delay’ in the text is justified by a ‘delay’ invoked by the text: an ‘idle period’ in an activity, a ‘breather,’ a ‘pause’” (ibid.). This is why love stories among stock brokers or auctioneers are not as common as those among cowboys, or gardeners, since love, like description, is something that grows in ease and leisure—with care, rather than hurry; with the rhythm of the daydream. There are fast-paced sections of Ann Veronica, without a doubt, but it is the slower ones, the ones that deal with the couple’s European vacation, in which descriptions are allowed the freedom to polychromatically shimmer, as in the excerpt shown in fig. 13.
By this time Capes’ hair had bleached nearly white, and his skin had become a skin of red copper shot with gold. They were now both in a state of unprecedented physical fitness. And such skirts as Ann Veronica had had when she entered the valley of Saas were safely packed away in the hotel, and she wore a leather belt and loose knickerbockers and puttees--a costume that suited the fine, long lines of her limbs far better than any feminine walking-dress could do. Her complexion had resisted the snow-glare wonderfully; her skin had only deepened its natural warmth a little under the Alpine sun. She had pushed aside her azure veil, taken off her snow-glasses, and sat smiling under her hand at the shining glories--the lit cornices, the blue shadows, the softly rounded, enormous snow masses, the deep places full of quivering luminosity--of the Taschhorn and Dom. The sky was cloudless, effulgent blue. Capes sat watching and admiring her, and then he fell praising the day and fortune and their love for each other. “Here we are,” he said, “shining through each other like light through a stained-glass window. With this air in our blood, this sunlight soaking us.... Life is so good. Can it ever be so good again?” Ann Veronica put out a firm hand and squeezed his arm. “It’s very good,” she said. “It’s glorious good!” “Suppose now--look at this long snow-slope and then that blue deep beyond--do you see that round pool of color in the ice- -a thousand feet or more below? Yes? Well, think--we’ve got to go but ten steps and lie down and put our arms about each other. See? Down we should rush in a foam--in a cloud of snow--to flight and a dream. All the rest of our lives would be together then, Ann Veronica. Every moment. And no ill-chances.”
It is ironic that Wells’s slightly caricatured portrait of Peter, Ann Veronica’s father, describes him reading “chiefly healthy light fiction with chromatic titles, The Red Sword, The Black Helmet, The Purple Robe, … in order ‘to distract his mind.’” (Wells, Ann Veronica 57), given that the novel is otherwise so colorful. In one scene, Manning professes his love to Ann Veronica by saying: “I want my life to be beaten gold just in order to make it a fitting setting for yours. … Forgive me if a certain warmth creeps into my words! The Park is green and gray to-day, but I am glowing pink and gold. It is difficult to express these things” (252). Wells paints a very bright, colorful scene, in which the lover’s pink skin is glowing with excitement, and his feelings—as shiny and as valuable as gold—emanate from him as if they were colors, and he were a source of light. This all contrasts with the “green and gray” of the inert vegetation against which it is set. Manning’s apology that “it is difficult to express” this shows how this kind of color description is so often rooted in reaching for fresh, unconventional ways of relating one’s visual experience.
That love and vision are close interlocutors is a view shared by H.D., in her theoretical work, Notes on Thought and Vision (H. D. (Hilda Doolittle) and H. D. (Hilda Doolittle)). There, she reminds us that “Socrates’s whole doctrine of vision was a doctrine of love. We must be ‘in love’ before we can understand the mysteries of vision” (22). She illustrates this with an imagined story of “the Galilean,” never explicitly named as Jesus, who
“fell in love with things as well as people. He would fall in love with a sea-gull or some lake-heron that would dart up from the coarse lake grass … He looked at the blue grass-lily and the red-brown sand-lily that grew under the sheltered hot sand-banks in the southern winter, for hours and hours. If he closed his eyes, he saw every vein or fleck of blue or vermilion” (28).
This attention to color is that which we expect to read of lovers, in love stories.
But we should remember that love stories are not disproportionately bright—that is, they don’t have more numbers of colors, simply more unique colors. If we quantify the total proportions of colors, we see a different story. Fig. 14 shows a subset of base color proportions for each LCSH. Here, psychological fiction is the most prominent, overall, although largely due to the incidence of black and white. The subject heading psychological fiction contains no fewer than ten works from Conrad, two each from Wells, Stevenson, Sinclair, Lawrence, Gissing, and individual novels from Woolf, West, and Maugham. James Joyce’s Ulysses is also notably present here. Conrad’s novels in this category are Heart of Darkness, The Secret Sharer, An Outcast of the Islands, Almayer’s Folly, Chance, Lord Jim, Victory, and The Nigger of the Narcissus. Gissing’s are New Grub Street and The Odd Women. Lawrence’s are Women in Love and The Lost Girl, Stevenson’s are The Strange Case of Dr. Jekyll and Mr. Hyde and The Master of Ballantrae. Wells’s are The Secret Places of the Heart and The Invisible Man. Sinclair’s are Life and Death of Harriett Frean and The Three Sisters. Also included are Joyce’s Ulysses, David Lindsay’s A Voyage to Arcturus, Lucas Malet’s The History of Sir Richard Calmady, Maugham’s The Moon and Sixpense, Neil Monro’s Bud: a Novel, Wests’s The Return of the Soldier, and Woolf’s Jacob’s Room.
Table 5 shows that the LCSH is somewhat hierarchical: double hyphens separate time periods (19th century), locations (England), and genres (Fiction). Genres also include “drama,” “poetry,” and “juvenile fiction.” I split out these commonly-occurring genre designations, programmatically, to form a new metadata column called “genre.” Then, I compute the same proportions of base colors, and group by these genres. Fig. 15 shows the result of the grouping based on this genre inference.
While fiction and drama have roughly the same proportions of colors, juvenile texts and poetry show nearly twice those numbers. This leads me to a theory that the most colorful fiction actually shows something we might call prose poetry. Alternatively, colorful fiction could be evidence of a childlike perceptual state on the part of the narrator.
Mark Doty, poet and author of The Art of Description, writes about what he calls “lyric time,” as a temporal element of the descriptive mode. Here, he notes how it’s a childlike state, in that it evokes a state in which causality and responsibility have not yet been eroded:
Lyric is concerned neither with the impingement of the past nor with anticipation of events to come. It represents instead a slipping out of story and into something still more fluid, less linear: the interior landscape of reverie. This sense of time originates in childhood, before the conception of causality and the solidifying of our temporal sense into an orderly sort of progression. (Doty 30)
Of course, writers of juvenile literature are not themselves children, but are writing to, and from, this state of mind. This is a state which attempts to convey the awe of early visual experiences. Before the names, purposes, and dangers of our immediate surroundings are known to us, they are first colors, sensations.
Which texts are the most colorful?
This observation about juvenile literature becomes apparent, too, at the level of the individual work. Table 6 below shows the total proportions of color expressions, if the text is more than two standard deviations away from the mean. I’ve annotated this list with some genres, where they are unambiguous. Many of these are childrens’ stories, and are full of bright colors and unfiltered perceptions. Many are collections of folk tales, some of which are for children. Padraic Colum’s are adapted from Irish folk tales, along with many of Lord Dunsany’s, and those alone account for five of the works on this list: both of these Irish writers were in some way involved in the Irish literary revival. Those works, and many others here, also belong, bibliographically, to the fantasy genre, in which highly imaginative, fantastic creatures or settings would need to be described. Many other works in this list involve travel of some sort (although that is perhaps an unsurprising trait of British literature of the early twentieth century). Travel to especially distant places overseas, whether real or imaginary, would also require thick description. Some of Katherine Mansfield’s stories are travel narratives in a different sense: although she writes about New Zealand as a native, she writes about it from England, and from a position of imagining something fictional happening in a very distant place.
| Filename | Author | Genres | totals |
|---|---|---|---|
| 1918-TheBoyWhoKnewWhatTheBirdsSaid-24493 | Colum, Padraic | Juvenile fantasy stories | 0.007085 |
| 1910-ADreamersTales-8129 | Dunsany, Lord | Fantasy stories | 0.006036 |
| 192156-MondayorTuesday-29220 | Woolf, Virginia | Short stories | 0.005861 |
| 1915-FiftyOneTales-7838 | Dunsany, Lord | Fantasy tales | 0.005699 |
| 1922-JacobsRoom-5670 | Woolf, Virginia | Novel, character study | 0.005434 |
| 1908-TheSwordofWelleranandOtherStories-10806 | Dunsany, Lord | Fantasy stories | 0.005222 |
| 1919-TalesofThreeHemispheres-11440 | Dunsany, Lord | Tales | 0.004885 |
| 1880-GreeneFerneFarm-37046 | Jefferies, Richard | 0.004771 | |
| 1922-CaptainBlood-1965 | Sabatini, Rafael | 0.004699 | |
| 1898212-TheTragedyoftheKorosko-12555 | Doyle, Arthur Conan | Travel novel, colonial | 0.004673 |
| 1895-TheSecondJungleBook-1937 | Kipling, Rudyard | Stories set in India | 0.004575 |
| 1922-TheWindBloweth-21999 | Byrne, Donn | Sea romance novel | 0.004568 |
| 191911-MaryOlivieraLife-9366 | Sinclair, May | Autobiog. novel | 0.004566 |
| 1922-TheGardenPartyandOtherStories-1429 | Mansfield, Katherine | Stories | 0.004558 |
| 1919-LivingAlone-14907 | Benson, Stella | 0.004509 | |
| 1922-TheHawkofEgypt-15721 | Conquest, Joan | Travel novel, Egypt | 0.004494 |
| 1887-TheFrozenPirate-22215 | Russell, William Clark | 0.004362 | |
| 1899-Findelkind-1367 | Ouida | 0.004227 | |
| 1918-TheReturnoftheSoldier-37189 | West, Rebecca | Novel | 0.004213 |
| 1892-TheNewMistressATale-32924 | Fenn, George Manville | 0.004028 |
Note that most of these are post 1910, and in fact, the mean year is 1910. Put differently, of the four decades’ of fiction seen here, from the 1880s to the early 1920s (where this corpus, for copyright reasons, stops), the last full decade is the one which contains the most positive outliers.
Which colors are the most prominent?
One of the first questions I asked of this data set was simply: what is the most common color in British literature of this period? This was, surprisingly, one of the most difficult questions to calculate, and necessitated the color categorizaton engine described above, since colors like light green needed to be counted as green, and colors like sky needed to be counted as blue. I hypothesized that the most common color here would be red, for its brightness, or green, for its ubiquity in flora. But I was very surprised to see that the most common colors are actually black and white. This is especially true once you keep in mind that not many other colors are descendants of the black and white categories: off-white is not very common in fiction, and neither are various other blacks. Grays belong to their own categories. So what could be causing this literary chiaroscuro?
Astoundingly, the color ranking shown below in fig. 15 follows the Berlin and Kay hierarchy from eq. ¿eq:berlinKay?: white is the foremost, then black, then red, and so on. This adds some evidence to the universalist view of the primacy of certain colors. But again, my object here is not to settle any linguistic debates, but to understand more of how color operates in literature.
Imagining individual texts
Zooming in to the level of an individual text will allow us to see how color, and description, operate within the narrative arc of a single work. Virginia Woolf’s To the Lighthouse is a perfect text for examining the role of color, since not only is it very colorful, but deals explicity with color perception. This is a novel that treats raw color perception, and raw sensation—as opposed to conventionally linguistic color writing—as transformative. On one of the first pages, we hear Mrs. Ramsay muse that “any turn in the wheel of sensation has the power to crystallise and transfix the moment upon which its gloom or radiance rests” (Virginia Woolf, To the Lighthouse 3). On the one hand, as a novel that deals with an artist and painting, this is an extreme example of the phenomenon I’m outlining in this chapter, but on the other, this is also the best example. Let’s start with how the novel treats painting, and its relation to its subject:
Mrs. Ramsay could not help exclaiming, "Oh, how beautiful!" For the great plateful of blue water was before her; the hoary Lighthouse, distant, austere, in the midst; and on the right, as far as the eye could see, fading and falling, in soft low pleats, the green sand dunes with the wild flowing grasses on them, which always seemed to be running away into some moon country, uninhabited of men. That was the view, she said, stopping, growing greyer-eyed, that her husband loved. She paused a moment. But now, she said, artists had come here. There indeed, only a few paces off, stood one of them, in Panama hat and yellow boots, seriously, softly, absorbedly, for all that he was watched by ten little boys, with an air of profound contentment on his round red face gazing, and then, when he had gazed, dipping; imbuing the tip of his brush in some soft mound of green or pink. Since Mr. Paunceforte had been there, three years before, all the pictures were like that, she said, green and grey, with lemon- coloured sailing-boats, and pink women on the beach.
There are many phenomena of note in this colorful passage. Although we are not in painter Lily Briscoe’s mind, in this descriptive narration, we nonetheless see this scene painted with many simple, primary colors: unmixed colors, straight from the tube. First, the water is so flat, or so blue, as to resemble a plate. Then, the green grasses, and the implied sand-color of the dunes. So far, this seems like a stereotypical seaside landscape. But even though this is a tableau, constructed precisely to resemble a painting, it is not at all static: the dunes are “fading and falling,” the grasses are “flowing,” and “running away.” Both the lighthouse and the grasses are anthropomorphized, to some degree. It is only when the painter Mr. Paunceforte approaches it that it is reduced to simple “grey,” “lemon-colour,” and “pink”: subdued, secondary colors. These are the colors against which Lily Briscoe rebels, in her own painting of these scenes. Here is Lily’s free indirect discourse:
The jacmanna was bright violet; the wall staring white. She would not have considered it honest to tamper with the bright violet and the staring white, since she saw them like that, fashionable though it was, since Mr. Paunceforte's visit, to see everything pale, elegant, semitransparent. Then beneath the colour there was the shape. She could see it all so clearly, so commandingly, when she looked: it was when she took her brush in hand that the whole thing changed.
In contrast to Mr. Paunceforte, who paints to create a pleasant, “elegant” painting, Lily pledges fidelity to the bright violet of the jacmanna, and thus to her own perception of the color, no matter how inelegant it might seem. We see these same colors appear moments earlier, when Lily, “with all her senses quickened as they were,” was “looking, straining, till the colour of the wall and the jacmanna beyond burnt into her eyes.” Lily is so devoted to faithfully conveying the color of this scene that she has allowed her eyes to unfocus, and her vision to blur, impressionistically, which softens the edges of the scene, and reduces it to just its colors.
Here again, textual colors are the vectors along which visual associations take place: transitions from one thought to the next. They enact a persistence of vision in prose. When Mrs. Ramsay imagines her son “all red and ermine on the Bench,”The court dress of Lord Justice Clerks, among other judges, is red and ermine.
that color is repeated in, or prompted by, the reddish-brown stockings that she knits for her son, only a paragraph later. Woolf suggests, then, through this chromatic association, that she knits him these stockings as an unconscious way of preparing him for a future career that she imagines for him. But the key is that she imagines him red, not his clothes, suggesting that this color impressionistically overtakes the image. It is a blur, a composite image, as in a dream.
When we examine the incidence of colors along the narrative time of the novel, as in fig. 16 with the x-axis representing ten sections from the novel’s beginning to its end, we see an overview of its narrative-descriptive arc.
The parts of the novel with the most color are undoubtedly the beginning and the end. But a close contender is the middle section 7, which, as readers of this novel have no doubt already guessed, aligns perfectly with the “Time Passes” section. This is a strikingly poetic segment of the novel, full of abstract language, nature imagery, and few people. As in poems, and poetic description, time is allowed to run wild: narrative, plot, and character become subservient to vision and perception. Again, although these images at times painting-like perceptions, they are extremely dynamic, as if a film is being played at four times its recorded speed.
The novel ends just as colorfully as it started, and with an appropriate image. Lily Briscoe finishes her painting, and looks at her canvas: “it was blurred,” we are told. Finally, Lily says, “I have had my vision.” Vision is a curious word, since it is almost always used metaphorically. The times we encounter a phrase like 20/20 vision in literature of this period are far outnumbered by the times we see vision used in the sense of imagination, prediction, plan, or clairvoyance, as in, for instance, Yeats’s A Vision, or H.D.’s Notes on Thought and Vision. These are all, paradoxically, modes of thinking, or of intuition, that don’t involve actual sight. But yet the superficial meaning of this term is that of seeing. This is more than a chance ambiguity, but a testament to the discourse between visual experience and thought.
Next Steps
Having seen ways in which color operates in description, ways which are analogous to, if not caused by, retinal cones, we now turn to those aspects of visual experience which are aligned with retinal rods: shapes, bodies, and forms. As in the retina, these categories are not mutually exclusive, but complement each other.
What makes modernist literature colorful?
Let’s skip ahead to the results of the experiment. Here is the distribution of images and colors in the narrative time of the novels of this period.
[Chart here]
The beginnings and the ends of the novels of this period are by far the most colorful, and the most consistently so. I will argue that this is due to three main factors, all of which are interrelated: descriptive scene-setting, childlike wonder, and prose poetry.
Some of these factors are generic: effects of the parent novel genre, the subject genre (romances, fantasies, Bildungsromane), or the audience (children’s literature). They are also writerly modes or techniques which perform those genres.
Genre
Bildung and portraits
A defining genre of the novels of this period is the Bildungsroman, a genre which Franco Moretti famously calls a “symbol” of modernity, in that modernity itself is a kind of society-level youth, self-mythologizing (Moretti 4).
I argue that visuality, and color in particular, are crucial to the modernist Bildungsroman. After all, the literal meaning of Bild is image, painting, or figure, and Bildung, usually translated education, means literally image creation or formation. It is no mere coincidence, then, that portraiture and self-portraiture are common themes of these novels, appearing even in the titles of Joyce’s A Portrait of the Artist as a Young Man, Wilde’s The Picture of Dorian Gray, and Pater’s Imaginary Portraits, to name a few. If we count Henry James, then we might add The Portrait of a Lady, and T. S. Eliot’s poem of the same name. Gregory Castle calls these “portrait” novels “portraits of aesthetic life,” and underscores their roots in the aestheticist literary movement, of which Pater and Wilde were visible figures, and which took place in the colorfully-titled Yellow Nineties (Castle).
Portraits are a genre of painting that are mimetic by definition: they aim to represent the image of their subjects. But the portraits of modernist Bildungsromane, however autobiographical, are impressionistic, representing instead the unfiltered worlds of sensory impressions, in order to mimic the visual experience of children, who have not yet mastered pragmatic lexical categorizations of their visual worlds. Bildungsromane often mirror this developmental phenomenology: being novels of education, they usually proceed chronologically, beginning with youth, and often from the viewpoint of youth. Joyce’s A Portrait of the Artist as a Young Man is the classic example of this narrative style, its first page constituting what Derek Attridge calls “one of the most revolutionary pages in the history of fiction” (Attridge). The page contains young Stephen Dedalus’s refracted sensory impressions. Among them is his lisped, misremembered version of his father’s apparent bowdlerization of H.S. Thompson’s song, “Lily Dale”:
On the little green place.
He sang that song. That was his song.
O, the geen wothe botheth. (Joyce et al. 5)
Simon Dedalus substitutes “place” for what is in Thompson’s song, “grave,” to make the song friendlier to children. The song’s chorus goes:
Now the wild rose blossoms
O’er her little green grave,
’Neath the trees in the flow’ry vale. (Gifford, Joyce Annotated 133).
This mistake is the first of many parapraxes which will become hallmarks of Joyce’s style. Young Stephen’s refraction, “o, the geen wothe botheth,” which makes the rose green, rather than the grave or place, appears several pages later, as an older Stephen muses in the classroom at Clongowes, where the class has been divided into white and red rose factions, an imitation of the English War of the Roses.
White roses and red roses: those were beautiful colours to think of. And the cards for first place and second place and third place were beautiful colours too: pink and cream and lavender. Lavender and cream and pink roses were beautiful to think of. Perhaps a wild rose might be like those colours and he remembered the song about the wild rose blossoms on the little green place. But you could not have a green rose. But perhaps somewhere in the world you could. [@joyce_portrait_2007 10]Green roses are of course possible with artificial coloring, and green carnations, as Joseph Valente and others have noted, were “an aestheticist emblem of imaginative artifice … and a badge of the homosexual subculture of fin-de-siècle England” (Valente 251). The Green Carnation is also the title of Robert Hichens’s 1894 roman à clef, first published anonymously, which satirizes Wilde and his coterie.
- and further suggests that “wild” may be a pun for “Wilde”
But in the geopolitical climate of Ireland of 1916—the year Portrait was published, and the year of the Easter Rising—green roses are hardly aesthetic-as-apolitical, that is, art-for-art’s-sake. Just as white and red roses symbolized the warring houses of Lancaster and York, green was a nationalist color for Irish homerule and independence. So when H. G. Wells’s 1917 review of Portrait celebrates the novel for its “too true” “account of the political atmosphere in which a number of brilliant Irishman have grown up,” an atmosphere he diagnoses as “just hate, a cant cultivated to the pitch of monomania,” his epithet for these young Irishmen is “bright-green”: colored by nationalism, but also naive, inexperienced (Wells, “James Joyce” 88).
Katherine Mullin notes that the heightened sensory detail of young Stephen’s narration “shows the interior thoughts of a character expressed in the language he might use at the time his thoughts are occurring,” a technique she identifies as “coloured narrative” (Mullin). The term is Graham Hough’s, from a 1970 essay on Jane Austen, and refers to a narrative mode akin to free indirect discourse (Hough 205). The color of the term is meant metaphorically, and does not refer to literal colors, but is nonetheless an apt metaphor for Stephen’s colorful prose: his narration is not only colored by his personality, as Hough hears in the narrative of Emma, but by the visual qualities of his heightened perception.
- White and purity?
Sinclair’s Mary Oliver
Prose poetry
Modernist novels are famous for their formal subversions.
Medium
Style
Descriptive scene-setting
- Generic invocations
Novels prime their readers to imagine by presenting them with visual descriptions.
[Draw on criticism of description]
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